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CAMBRIDGE TALENT STUDY
DECEMBER 2018
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AGENDA
CAMBRIDGE TALENT STUDY
Talent Stack of the Job Families analysed, Job role/Skill level analysis
Industry Level Analysis : Top peer employers, workloads, talent maturity analysis and salary distribution
Start-up Deep Dive: Analysis of Funding, Acquisitions and top start-ups across technology areas
University Analysis: Talent supply analysis, Professors profiling
Overview of the Cambridge ecosystem
Major Talent Hotspots : Hotspots of peer employers
Career Progression analysis: analysis of adjacent talent and Demand supply gap analysis
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Executive Summary (1/3)
- Technology companies are embracing the concept of “Micro hubs” wherein they take advantage of talent across the globe, even if it is in
smaller numbers in some instances. There are several examples of “Micro hubs” in Cambridge, UK including companies like Microsoft,
Samsung and Apple.
- It is at this juncture Draup studied Cambridge, a diverse ecosystem offering wide range of possibilities across different fields. A mix of Hi-
tech, Healthcare and University talent has contributed tremendously to the evolution of Cambridge
- The uniqueness of this study is the depth of talent analysis done across industries, peer companies, technologies, job families and skills.
We believe that this will provide us an overall view of the state of the talent both on the supply and demand side.
- Our methodology included detailed analysis of open job descriptions across the last 3 to 5 years and develop a consolidated job corpus
data for mining purposes. Further to this analysis, we conducted multiple interviews and discussion sessions to understand the current
economic rationale of Cambridge, emerging technologies, talent spread and potential unicorns.
- The roles across the job corpus were organized into 8 roles – AI/ML, NLP, Computer Vision, Security, S/W Development, UX/UI, Cloud
and IoT/Hardware. This is essential for us to triangulate a huge data corpus to a reasonable number of dimensions in order gain deeper
insights.
- Cambridge, also known as “Silicon Fen” is organized as multiple clusters
- North Cambridge - Hotspot for Software/Internet, Semiconductor and Life Science Employers. It has Cambridge Science Park
and St. John’s Innovation Park
- South Cambridge - Healthcare and Life science Hotspot. It has Cambridge Biomedical Campus, hub for ~34 biomedical firms
- West Cambridge – Academia. University of Cambridge is located here.
- East Cambridge – Non knowledge intensive businesses e.g.- manufacturing and production industries
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Executive Summary (2/3)
- Our analysis shows the total workforce of Cambridge is ~110,000 in 2017. Out of which 86,000 talent has been mapped across non-
IT(68,000) and digital(18,000) job families. Academic and Medical professionals employ ~50% of the non-IT talent where as Software
Development and IT Infrastructure employ ~48% of the digital talent.
- Algorithms, Data Science and Deep learning are the top skills in AI/ML Job family; Cloud Computing, Network security & management,
Ruby and Information security are the top 4 skills across Cloud and Security Job families.
- Job Demand : S/W Development and AI/ML are the top job families with most open positions, cumulatively contributing to ~62% of total
demand across ~3400 Job openings. Enterprises in Software/Internet and Healthcare industries, hired the largest talent pool across
AI/ML, Security and UX/UI Job families over the past 6 months.
- Salary Analysis : Computer Vision and NLP are the highest paid professionals across all job roles analysed.
- Installed talent distribution and technology focus across top 3 industries :
- Enterprise/Software : Software Engineers constitute 60% of the total digital talent. Predictive Analytics, Human computer interaction
and 3D Modelling/Mapping are the major focus areas in AI/ML
- Hardware/Semiconductor : IoT Engineers constitute 47% of the total digital talent. Embedded System Software and Autonomous
Driving are the major focus areas across IOT
- Life Sciences : Software Engineers constitute 37% of the total digital talent. Health Data Analytics, Gene Sequencing and Chronic
Rare Diseases Management are the emerging capabilities in health care with high AI/ML innovation
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Executive Summary (3/3)
- Cambridge is home to ~210 start-ups which has seen a 30% YoY growth with a cumulative funding of ~$1 Bn; ~47% of the funding in
2017 was received by Healthcare sector, followed by Software/Internet (38%).
- Tech giants have made landmark acquisitions from companies spun out from University of Cambridge. Major acquisitions have happened
around ML/NLP/Computer Vision and Security segments.
- Considering the talent supply, a detailed analysis of over 6 premium universities in Cambridgeshire showed that the fresh
Software/Computer graduates were around 900.
- The universities in Cambridge are responding well to the changing demand of tech companies by introducing niche courses in computer
vision, natural language processing, neural computing, network simulation and modelling etc., compared to the rest of the world
universities.
- Top tech companies have predominantly collaborated with University of Cambridge for co-innovation, skill – enhancement and setting up
of digital labs/research centers.
- Upskilling the existing technical workforce into advanced Data Science/AI/Cloud/Security roles is being done through certifications
provided by universities and top tech companies. Apart from University of Cambridge, this is currently accomplished through partnerships
with training institutes such as Cambridge Spark and online platforms such as Khan Academy and Coursera.
- We have also conducted deep analysis across different technology segments
- The type of products, partnerships, acquisitions, collaborations and scholarships by big companies - Hiring Difficulty analysis
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CAMBRIDGE - OVERVIEW
Cambridge is the fastest growing economy in the
UK with an annual year-on-year growth rate of
~2.2%. While the employment rate has increased
by 0.8% during 2017-18
In this report, DRAUP has analysed the Cambridge talent
ecosystem comprising of Top enterprises, Startups and
Universities
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~110,000Total Employment (2017)
Insights
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About this report
~11,700Total Employers
~18,000*Digital and IT Talent
1Cambridge cluster generates 12.3 Billion Euro which constitutes ~4% of the UK GDP. The cluster
employs ~110,000 workforce spread across 11,700 companies.
1Cambridge has ~18,000 digital talent pool which is largely employed across Software/Internet,
Semiconductor, Biotech enterprises and ~1200 mid scale organizations and start-ups
1Nearly ~13,500 talent is employed across 7 digital technologies – IOT, AI/ML, Cloud, Software
Development, Security, Analytics and UI/UX design
~900 Software students graduate from University of Cambridge and Anglia Ruskin University. Top
Golden triangle universities i.e. Imperial collage London, Oxford university and University Collage
London supply a large talent pool
Note: The represented data has been analysed using DRAUP Proprietary Talent Database 6
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Cambridge has a total workforce of ~110,000, majority of which is employed in Education, Human Health & Social, and Professional Scientific & technical sectors
Source: DRAUP talent module was leveraged to analyze employment across industries
Unemployment rate* ~2.73%
Median age ~30 Years
Sex ratio 48 Female/52 Male
GDP ~$10.64Bn
GDP growth rate 2.19%
Median Household Income $65,660
Percentage of workforce across different industries
Total Workforce : ~110,000Cambridge is the 3rd largest Healthcare cluster in the worlds
after Boston and San Diego
32 Colleges and Research Institutions in Cambridge employ
~21% of the overall talent
Cambridge Science park is the hub for Hi-tech companies
Manufacturing construction and utility companies employ
5% of the total employed talent
Population: ~1,24,900
Non-Cambridge
Workforce*:~38.6%
Top Spoken Languages: English, Spanish, French, Chinese,
Ethnicity: White British(73.5%)
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Silicon Valley of the UK, dubbed as “Silicon Fen”, is broadly clustered into Hi-tech(North), Life science(South), Academia(West) and non-knowledge intensive businesses(East)
Hotspot
Enterprise/Software
Companies
Semiconductor/Telecom
munication Companies
Pharma & Healthcare
Companies
Tech Giants
INDEX
Redgate
Software
Cambridge Wireless
NORTH CAMBRIDGEHotspot for Software/Internet, Semiconductor and Life Science
Cambridge Science Park, St John’s Innovation Park TOP COMPANIES – Beyond Media Limited, GW Pharmaceuticals
Major life science companies are moving towards South since
South Cambridge is an active Healthcare innovation hub
WEST CAMBRIDGEACADEMIA – University of Cambridge
UC is relocating its Engineering, CS and AI/ML departments to the vicinity of tech companies
SOUTH CAMBRIDGE
Healthcare and Life science Innovation HotspotCambridge Biomedical Campus (34 Biomedical Companies)
TOP COMPANIES – AstraZeneca, GlaxoSmithKline, Abcam
EAST CAMBRIDGE
Non-knowledge intensive businesses Manufacturing and Production industries are consolidated in Eastern Cambridge
IBM
AVEVA Audio Analytic
Cycle
Pharmaceuticals
GW
Pharmaceuticals
Napp Pharmaceutical
Group Ltd
Citrix
Cambridge
Apple Siri R&D Centre
Samsung R&D
Centre
Microsoft R&D
Centre
AstraZeneca
Qualcomm
Toshiba
Illumina
GSK
Broadcom
Philips
Healthcare
Darktrace
Cambridge Intelligence
Amgen LimitedMyrtle Software
Cambridge Biomedical Campus
Cambridge Science Park
St John’s Innovation Park
Universities
University of
Cambridge
Anglia Ruskin
University
Huawei
Abcam
AstraZeneca
Cancer ResearchAdenbrooke’s
HospitalARMS
Holding
Note : DRAUP’s proprietary talent module was used to analyse hotspots by locations and Industry wise
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Employment opportunities, low cost of living and extensive research ecosystem attracts talent towards Cambridge
Cambridge
London
Oxford
Non-Resident Commuters
Cambridge has high in-commuting workforce, constituting ~35% of the overall
employment; Majority of commuters are from Cambridgeshire~38,500In-Commuters
Seamless Connectivity
The average journey time between Cambridge-London and Cambridge-Oxford is ~1
hour. There are 184 trains per day travelling from London to Cambridge~1 hr
Commute time
“Golden Triangle” - Global Research Hub
Cambridge is known as Silicon Fen with strong university background in bioscience and
software, golden triangle has a total of ~22,700 resident graduates~22,700Graduates
Relatively low Cost of Living
~12% Lower cost of living than that of London; 20% lower Housing cost in Cambridge
compared to London~12%
Lower Cost of living wrt UK
High Paying Jobs
Cambridge’s average salary is £35,900, which is ~32% higher than the national average~32%Higher than national avg
University of Oxford
University Collage London & Imperial Collage London
University of Cambridge
Note : DRAUP’s proprietary talent module was used to analyse the talent attraction towards Cambridge 9
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Non-IT Job Families: Academics and Medical Professionals employ ~50% of the non-IT talent
Employed Talent in Non-IT Job families
~6600
~23000
~7000
~4000
~900~2500 ~1700
~11000
~1700 ~2500 ~2000
~5000
Core Engineering
Academics Operations Sales FinanceMedia &
Communication
MarketingMedical
ProfessionalsHuman
ResourcesArts & Design
Social Service
Consulting & Business
Development
Total Talent : ~68,000
Mechanical Engineer
Telecom Engineer
Civil Engineer
CAD Engineer
Professor
Counsellor
Curriculum Developer
Assessment Associate
Line Supervisor
Purchase Specialist
Supply Chain Analyst
Production Manager
Sales Representative
Account Executive
Business Developer
Sales Analyst
Financial Analyst
Trader
Quantitative Analyst
Portfolio Manager
Media Planner
Social Media Manager
Public Relations
Officer
Web ContentManager
Content Strategist
Marketing Data Analyst
Digital Brand Manager
SEO Specialist
Medical Assistant
Doctor
Pharmacist
Dietitian
Recruiter
Labour Relations Specialist
Compensation Analyst
Training Manager
Animation Expert
Advertising Specialist
Architect
Fashion Designer
Social care manager
Community Worker
Psychiatric Social Worker
House Manager
Client Retention Manager
Executive coach
Strategy Consultant
Digital Transformation
Expert
Note: The represented data has been derived using DRAUP Proprietary Talent Database 10
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Digital Job families: Software Development and IT Infrastructure employ ~48% of the total talent
IT Infrastructure
TestingIoT/Embedded
/HardwareCloud
Analytics/BI
Software/Web/Mobile Development
Data Science/ ML
UI/UX DesignSecurity
900 500 1,700 5,300
2,600
600 2,000
1,100 3,300
Employed Talent
Total Talent across 9 tech job families: ~18,000
Employed talent in Digital and IT Jobs across 9 Job Families
Security Engineer
Penetration Tester
Incident Handler
Security Analyst
UX Designer
UX Researcher
UI Developer
Interaction Designer
Data Scientist
ML Engineer
Applied Scientist
Interaction Designer
Software Engineer
Full stack developer
Web Developer
IOS Developer
Data Analyst
Risk Analyst
Visualization Analyst
BI Analyst
Cloud Engineer
Cloud Enterprise Architect
Cloud Applications Engineer
Cloud Infrastructure Engineer
Embedded Software Engineer
Firmware Engineer
Compiler Engineer
IoT Developer
Test Engineer
SDE in Test
Test Automation Engineer
IT Test Analyst
Infrastructure Engineer
IT Infrastructure Developer
Systems Engineer
Network Engineer
Note: The represented data has been derived using DRAUP Proprietary Talent Database 11
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Top Skills: Large proportion of employed talent pool is skilled in programming languages such as C/C++, Python and SQL
J2EE 200 Hadoop 200 Swift 180 Ruby on Rails 160 Chef 150
C/C++ 7,000 Python 3,700 SQL 3,500 Java 3,100 MATLAB 2,800
Perl 1,400 Git 1,300 .NET 1,150 Shell 1,000 jQuery 900
Open Source 850 Shell Scripting 700 Eclipse 550 DNS 500 Jenkins 400
Bayesian 400 Ruby 350 Node.js 300 Deep Learning 270 SAS 250
HTML 2,600 JavaScript 2,250 Machine Learning 1,600 XML 1,500 PHP 1,450
Hig
hM
ediu
mLo
w
Note: The represented data has been analysed using DRAUP Proprietary Talent Database 12
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Top Skills - AI/ML- Algorithms, Data Science and Deep learning are the top skills in AI/ML Job family
AI COMPUTER VISION NLP
550500
1350
1600
850
150100
900
200150
100
StatisticalModelling
PredictiveModelling
Data Science Algorithms Image Processing OpenCV Machine Vision Deep Learning SpeechRecognition
Text Mining ComputationalLinguistics
Statistical Modelling
Predictive Modelling
Deep Learning
Image Processing
OpenCVMachine
VisionText Mining
Computational Linguistics
Speech Recognition
AlgorithmsData
Science
Note: The represented data has been analysed using DRAUP Proprietary Talent Database 13
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Top Skills - Cloud and Security: Cloud Computing, Network security & management, Ruby and Information security are the top 4 skills across Cloud and Security Job families
550
270
230
300
170140
180
350
530
350
150
Cloud Computing Virtualization ITIL Firewall VPN TCP/IP JSP/J2EE Ruby Network Security& Management
InformationSecurity &
Management
ISO27001
SECURITY
JSP/J2EE Ruby
Network
Security &
Management
Information
Security &
management
ISO27001Cloud
ComputingVirtualization ITIL
CLOUD INFRASTRUCTURE
Firewall TCP/IP VPN
CLOUD SECURITY CLOUD APPLICATION
Note: The represented data has been analysed using DRAUP Proprietary Talent Database 14
Digital Job Demand: Cambridge has a total Digital demand of ~3400; S/W Development and AI are the top Job families contributing to ~62% of total demand; Time to hire is highest for Computer Vision
Key Job Roles Open PositionsTime to Hire
(Days)Key Job Titles Key Employers
AI /ML ~620 ~45Machine Learning Engineer, Machine Learning Specialist, Deep Learning Engineer, Machine Learning Platform Engineer
Amazon, Apple, Arm, Microsoft, Prowler.io
NLP ~50 ~40Applied Scientist, NLP Engineer, NLP Research Scientist, Language Engineer
Amazon, Apple, Huawei, Harnham
Computer Vision ~100 ~60
Computer Vision Engineer, Computer Vision Scientist, Applied Computer Vision Scientist, Software Engineer (Computer Vision), Computer Vision Research Engineer
Amazon, Arm, Huawei, Microsoft, Nvidia
Security ~100 ~40Information Security Manager, Information Security Engineer, Cyber Security Engineer
Amazon, Arm, AstraZeneca, Genomics, Google, Microsoft
S/W Development ~1500 ~30Software Developer, Android Software Developer, Application Developer, Front End Developer
Amazon, Arm, Citrix, Microsoft, Qualcomm
UX ~200 ~25UX/ UI Designer, UX Architect, UI Software Engineer, UX Researcher, UI Developer
Amazon, Arm, Microsoft, Redgate, Lucidworks
Cloud Engineer ~500 ~40Cloud Architect, Cloud Software Development Engineer, Cloud Systems Engineer, Cloud Devops Engineer
Citrix, Genomics, Microsoft, Prowler.io, Nokia, Qualcomm
IoT ~350 ~45IoT Test Automation Engineer, IoT Software Engineer, System Test Engineer (IoT)
Arm, Nokia, Qualcomm
Note: Data Science is considered in AI/ ML job role 15
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Recent Hiring Analysis*: Enterprises in Software/Internet and Healthcare industries hired the largest talent pool across AI/ML, Security and UX/UI Job families over the past 6 months
AI/ ML NLPCOMPUTER
VISIONSECURITY
S/W
DEVELOPMEN
T
UI/UXCLOUD
ENGINEER
IOT
EMBEDDED
Software Internet
Healthcare/
Biotechnology
BFSI
Automotive
Telecommunicati
on
Electronics/Semi
conductor
Top Employers
Microsoft, Oracle
AstraZeneca, Sogeti, 10x Genomics
Citi, HSBC, UBS, Bank of England
Jaguar Land Rover, Vindis Group
Huawei, Virgin Media Business
Arm, Qualcomm, Citrix
Low Medium High
Total 1,300 professionals hired for following new age job roles across different industries across past 6b months
0 400Talent size
Note : DRAUP’s proprietary talent module was used to analyse technology talent across different Industries. The Talent count is inclusive of Applied Scientist counts in each of the domains 16
Cost Analysis: High demand for Data science and Software developer talent is driving the high Salary for these Job roles
Note : DRAUP’s Talent Simulation Module
Med
ian
Sa
lary
(U
SD)
per
an
nu
m
£40,000
£30,000
£35,000 £35,000£33,000
£30,000
£45,000 £45,000
£52,000
£45,000£42,000
£47,000
£42,000 £41,000
£55,000£58,000
£70,000
£60,000
£70,000
£65,000
£55,000 £55,000
£75,000£78,000
Data Science Software Cloud Security UI/UX Design IoT Computer Vision NLP
Entry (0-5) Middle(5-10) Senior (10+)
£42,000 £58,000 £60,000£54,000 £45,000 £49,000 £49,000 £43,000
XX,XXXAverage Salary
Note : DRAUP’s Talent Simulation Module17
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Digital Talent Distribution: 72% of the Digital talent is employed by Enterprises; Software internet, Semiconductor and Healthcare are the major industries constituting 86% of the installed talent
53%
20%
13%
<5% <5%<10%
Software/ Internet Semiconductor Healthcare Telecom Financial Services Others
Total Digital and IT Talent
~5000
Total Digital and IT talent across Enterprises :
~ 13,000
Top Employers Top Employers
Enterprises1 Start-ups2
Note: Others include talent from industries like Electrical Engineering, Mechanical Engineering, Aviation etc. 18
Enterprise Analysis
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Top Employers by Job Openings: AstraZeneca, ARM, Microsoft and Qualcomm have the highest job openings for Data Scientist, AI/ML and software development roles
Peer
Employers
Open
Jobs
Data Science
/ML/AISecurity
Software
DevelopmentUI/UX Cloud
IoT/
EmbeddedAnalytics/BI Centre Product Focus
~40
ML Engineer,
Knowledge
Engineer, Data
Scientist
UX Designer/
Researcher
Data & Analytics
Engineer,
Genomics D&A
Engineer
ONCOLOGY
BIOINFORMATICS, OPTIMIZE
CLINICAL DEVELOPMENT
PROGRAMS
~100
Principal Security
Architect, Security
Researcher,
Information
Security Risk
Engagement
Manage
Solution Architect,
Software Engineer,
Lead Java Web
Developer
UX Designer
Firmware
Development
Lead, Compiler
Engineer, Software
Engineer-
Prototyping,
Design Engineer
Software Engineer,
Engineering
Analytics
ENGINEERING ANALYTICS
TEAM, SECURITY RISK
MANAGEMENT AND
COMPLIANCE (SRMC) TEAM
~45
Data Scientist,
ML/AI Engineer,
Software Engineer
Machine Learning,
Deep Learning
Researcher
Security Research
Software Engineer
Software
Development
Engineer
Web Development,
UX Design
Cloud Software
Engineer
Graduate
Hardware
Engineer
FEMTOSECOND LASERS TO
STORE DATA IN GLASS, MS
EXCEL, CONFIDENTIAL
COMPUTING, HEALTHCARE AI
PRODUCTS
~10
Senior Software
Engineer, IT
Engineer,
Software Product
Manager
Application
Engineer,
Engineering
Technician, RF
systems Engineer
CDMA TECHNOLOGY, DIGITAL
DESIGN TEAM, RF SYSTEMS
TEAM
Open Job Area Intensity High Low
Note : DRAUP’s proprietary talent module was used to analyse peer employers by skill type 20
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Strategic focus : Tech giants, Semiconductor and Life sciences companies have technology focused teams in Cambridge;
Pharma/Biotech firms in Cambridge are focusing on Bioinformatics and Genomics
Life Sciences & Biotech start-ups have received ~$470 M funding, highest among all industries
Semiconductor firms are leveraging Cambridge to build end-to-end IoT capabilities
Tech Giants have set up Cambridge Centres for it’s high quality research in Artificial Intelligence/ ML/ Computer Vision/NLP
Software/Internet
Semiconductor Scaled Startups
Acquired Cambridge Silicon Radio (CSR), The acquisition complements Qualcomm's current offerings by adding products, channels, and customers in the growth categories of Internet of Everything (IoE) and automotive infotainment.
Pharmaceutical / Biotech
Acquired VocalIQ, which uses ML, NLP to build virtual assistant, the product was integrated into Siri. Apple has used the acquisition as a base to expand it’s presence
Set up an AI-research centre in Cambridge. Major focus on Computer Vision and ML. Working on Automatic Human Behaviour Analysis.
Set up it’s research centre in 1997, and has since scaled to 130+ researchers and engineers working across data centres, cloud and healthcare
ARM acquired Silicon valley based Treasure data, an enterprise data management company to build a device-to-data IoT platform Pelion. It’s earlier acquisition of Stream for connectivity management, allows it to offer devices management, connectivity management and data management in it’s platform , complementing it’s IoT hardware capabilities
Mission Therapeutics has raised ~$127m for it’s drug discovery and development platform
Crescendo Biologics has raised ~$116m. It is a biopharmaceutical company developing potent, truly differentiated Humabody® therapeutics in oncology with a focus on innovative targeted T-cell approaches.
Bicycle therapeutics has raised ~$90m,it’s a biotechnology company pioneering a new class of therapeutics based on its proprietary bicyclic peptide (Bicycle®) product platform, with a major focus on Oncology
Collaborated with Cancer Research UK to setup a Functional Genomics Centre, work on genetic screening, cancer modelling and big data processing aimed at accelerating the discovery of new cancer medicines
Collaborated with California-based Innovative Genomics Institute (IGI) to use CRISPR to uncover genes and disease pathway mechanisms involved in DNA Damage Response (DDR)
Astrazeneca has adopted the Horizon Discovery’s Edit-R™ crRNA libraries as part of a drive to establish a functional genomics discovery platform.
Note : DRAUP’s proprietary talent module was used to analyse strategic focus across different Industries
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Software/Internet - Tech Giants Team Structure:Tech giants have hired University professors and leaders from peers for leading their product teams
Talent Distribution
~ 20%
~ 60%
~ 20%
Total R&D Size in Cambridge
ResearcherCTO, (Hypertag)PhD, University of Cambridge
Previous Experience
Hiring
Focus from
PRODUCT LEADERSHIP
MID LEVEL ROLES
Chief Research Scientist
Software Development
Engineer
Technical Support Engineer
Staff Engineer
Speech and Machine Learning
Research Scientist
Machine Learning Engineer
Partner Scientist
Principal Scientist
~10-12Average Product
Team Size
Principal Software Engineer
Principal Architect
Product Team Structure~30 ~40 ~100
Un
ive
rsit
y
Sta
rt-U
ps
Pe
ers
Chief Research ScientistProfessor of Computer Science (University of Edinburgh, Aston University)
Senior ScientistResearch Director (University of Cambridge)
Partner ScientistPhD, AI (University of Edinburgh)
Data and Applied ScientistLanguage Processing Engineer, (Swiftkey)Mphil (University of Cambridge)
Principal Applied ScientistSenior Researcher, (Fraunhofer-FIRST)
ML EngineerStatistician (deCODE genetics)
Machine Learning EngineerMphil, ML,Speech & Language Technology (University of Cambridge)
Speech and Machine Learning
Research ScientistSenior NLP Researcher (VocalIQ)
ML EngineerDeep Learning Research Engineer, (Smart Eye)
Technical Program Manager - SiriWeb and Mobile Development, (HP)
NLP Dialogue EngineerPhD in Computer Science(University of Cambridge)
Software Engineer
Software Engineer (Google)
ENTRY LEVEL ROLES
Chairman Samsung AI ResearchProfessor, Machine Intelligence (University of Cambridge)
Program Director, Samsung AIPrincipal Scientist, (Nokia Bell Labs)
System Team Lead Principal Engineer, (CSR)
Principal NL Research Engineer(Nuance Communications)
Senior Engineer -SAICPrincipal NL Research Engineer(Nuance Communications)
Senior Researcher -SAICResearch Scientist (Nokia Bell Labs)
Hired from Start-UpsHired from University Hired from PeersLegend:
Note : DRAUP’s proprietary talent module was used to analyse product team structure of tech giants
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Software/Internet: Software developer Job family constitutes majority of the employed digital talent; Tech Giants have high focus on AI/ML and NLP
100 - 120
Focus Areas: Hybrid Cloud Application, Multi-Cloud
networking, analytics intelligence with machine learning,
Virtualization technologies
30 - 40
Focus Areas: Game Design , Game Mechanics ,
Console Development, Build and support game
systems, work with COBRA engine, Graphic Design
150 -200
Focus Areas: Biological Computing, Human Experience
& Design, Machine Intelligence and Perception
Project Emma - Help Person suffering from Parkinson’s.
Project Torino - physical programming language for
children with vision impairments
250- 300
Focus Areas: MATLAB, Simulink product
development and support, Automatic Code
Generation, Deep Learning Development
130-150
Focus Areas: Natural Language Processing,
Automotive Voice Recognition, Speech Recognition
Siri - Integrate machine learning models with Siri’s
Architecture
Products/Technologies
20%
AI/ML CloudSoftware
DeveloperSecurity UI/UX IoTNLP
15%
0%
5% 5%
5%
0%
10%30%
40%
50%
50%
10% 0% 0%50%
10%
5%
0%
30% 0%
5% 0%
10% 0%
0%
0%
5% 5% 5%
0% 25%
0%20%
10%
0% 0-25% 25%-50%
Note : DRAUP’s proprietary talent module was used to analyse jobs by top companies and skill type 23
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Software/Internet: Talent Maturity Analysis - Over 65% of the employed digital talent in Security and IOT Job families have 10+ years of experience
35%
24%
13%8%
31%
15%
45%53%
22%
21%
33%
17%
25%
20%
22%
20%
43%
55% 53%
75%
44%
65%
33%27%
Data Science Software Engineer Cloud Security UI/UX IoT Computer Vision NLP
0-5 Years 5-10 Years 10+ Years
Employed Talent Distribution Across Job Roles and Years of Experience
~650 ~2,600 ~300 ~300 ~280 ~200 ~250 ~100
Enterprise/Software Talent~4,300
Employed Talent is
(analyzed job roles)
Note: Talent size for NLP and computer are included in Data Science Job family
Embedded Job roles are included in analysis of IOT Job family
Note: The represented data has been analysed using DRAUP Proprietary Talent Database 24
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Software/Internet : Technology Focus - Predictive Analytics, Human computer interaction and 3D Modelling/Mapping are the major focus areas in AI/ML
Mid scale software companies are developing Behavioural Learning, Analytics and simulation technologies for Virtual environments. FiveAIis leading the development of Autonomous Driving software systems in Cambridge Area
Citrix is Developing new security analytics service to combat insider security threats and detect malicious activities using data from Citrix systems such as NetScaler MAS, OctobluIoT
Microsoft is developing AI-based Navigation and Positioning systems, also researching on Human Interaction and Biological Computation
AI/ML, Computer Vision, Natural language Processing and Cybersecurity are key focus areas for Software/Internet companies in Cambridge
INSIGHTS
Major technology companies like Apple and Amazon have set-up small-scale teams for research and development of Automatic Speech Recognition & Language Translationfor Siri and Alexa Products.
Hiring Matrix
Function of the number of Current Job Openings and
Last 1 year hiring as percentage of Employed talent
Talent Maturity
Function of employed talent pool size
Low
High
Em
plo
ye
d T
ale
nt
Po
ol
Ind
ex
High
Predictive
Analytics
Cybersecurity
Human-
Computer
Interaction
Biological
Computation
Behavioural
Learning &
Simulation
Navigation &
Positioning
Automatic
Speech
Recognition
Chatbots
Language
Translation
Natural
Language
Understanding
Real-time
Threat
Monitoring Autonomous
Threat
Response
Cloud
Security
Object
Identification
& Tracking
3D Mapping
3D Modelling
Visual Text-
to-Speech
Game
Development
Enterprise
Applications
Systems
Software
Mobile
Applications
Data
Visualisation
Softwares
Hiring Matrix
Nascent CapabilitiesNiche Capabilities
Emerging CapabilitiesMature Capabilities
Sound
Recognition
Autonomous
Vehicles
Image
Enhancement
S/W Dev.
Security
AI/ML
Cloud
Computer
Vision
IoT
UI/UX
Design
NLP
Note: The above analysis is based on the DRAUP’s proprietary engineering database25
26
Semiconductor Industry : Software developer Job family constitutes majority of the employed digital talent; ARM, Qualcomm and Samsung also have high focus on IOT
Products/TechnologiesAI/MLComputer
Vision
Software
DeveloperSecurity UI/UXNLP
IoT/Embe
dded
400-450Primary focus is on pioneering IoT and automotive
technologies, with engineering areas like Analogue and
Digital Design, Voice and Music (Hardware, Software, OEM
Support, Innovation) and Advance Systems
170-200
The AI research centre targets "Human-Centric AI“ for
areas such as emotion recognition and medical
diagnostics. Other Focus Areas include WiFi Firmware,
Radio Firmware and developing connectivity IP for SoC
chips.
800-900Primary focus is on designing scalable, energy efficient-
processors and physical intellectual property (IP) for
sensors, servers, smart phones, tablets, enterprise
infrastructure and the Internet of Things.
80-100Toshiba CRL focuses on Quantum Information, Quantum
Key Distribution and Semiconductor quantum dots, Speech
Technology, automatic speech recognition and Computer
Vision.
SPINEX : AI Platform for B2B applications
50-60
Key focus is on software and hardware development for next
generation mobile multimedia products including
innovations in high-performance graphics, video, audio and
gaming on portable devices, mobile TV, DSL modems and
next-generation broadband technologies.
80-100
100-150
450-500
30 - 40
20-30
30% 5%
5%
40%
0%0%
10%
10%
5%
0% 0%
6% 4%
0%
5%
70%
60%
0%
40%
30%
40%
75%
0% 4%
2% 0% 10% 10%
0% 0%
4%
5%
30%0%
10%
0% 0-25% 25%-50% >50% Engineering Talent Digital Talent
Note : DRAUP’s proprietary talent module was used to analyse jobs by top companies and skill type26
27
Semiconductor Industry - Talent Maturity Analysis: Over 65% of the employed digital talent in Security and IOT Job families have 10+ years of experience
29% 32% 33%
15%
33%
14%
29% 21%
33%
20%
33%
16%
42%47%
33%
65%
33%
70%
Data Science Software Engineer Cloud Security UI/UX IoT
0-5 Years 5-10 Years 10+ Years
~150 ~600 ~50 ~150 <50 ~900
Employed Talent Distribution Across Job Roles and Years of Experience
Semiconductors Talent~1,900Employed Talent is
(analyzed job roles)
Note: Talent size for NLP and computer are included in Data Science Job family
Embedded Job roles are included in analysis of IOT Job family
Note: The represented data has been analysed using DRAUP Proprietary Talent Database 27
28
Semiconductor Industry - Technology Focus: Embedded System Software and Autonomous Driving are the major focus areas across IOT
Other emerging research areas include development of AI-based touch sensors (Cambridge Touch Technologies), IoT SoC Systems (ARM), Image Enhancement and Object Tracking Processors (ARM)
Qualcomm and ARM are also developing IoT platforms as well as software development kits for applications in Autonomous Mobility, Connected Devices and Telecommunication devices such as Modems
Major semiconductor companies like ARM and Qualcomm are focusing on developing end-to-end capabilities. With focus on developing data management, device management and connectivity management services
INSIGHTS
ARM is leveraging Artificial Intelligence & Machine Learning technologies for developing ML-based processors to increase the performance and efficiency of smartphones, autonomous vehicles and data centers
Hiring Matrix
Function of the number of Current Job Openings and
Last 1 year hiring as percentage of Employed talent
Talent Maturity
Function of employed talent pool size
HighLow
High
Em
plo
ye
d T
ale
nt
Po
ol
Ind
ex
Hiring Matrix
Automatic
Speech
Recognition
Chatbots
Voice
Recognition
Autonomous
Driving
Sensors
Embedded
Systems
Softwares
Embedded
OS
Embedded
Testing
Softwares
IoT SoC
Systems
Connected
Vehicles
Image
Enhancement
Object
Identification
& Tracking
Nascent CapabilitiesNiche Capabilities
Emerging CapabilitiesMature Capabilities
3D Modelling
Connected
Devices
ML-based
Processors
S/W Dev.
Security
AI/ML
Cloud
Computer
Vision
IoT
UI/UX
Design
NLP
Note: Analysis is based on the DRAUP’s proprietary engineering database28
29
Life Sciences : Cambridge is the third largest Biotech cluster in the world, attracting top tier companies to set up research centres and leverage the Bio-tech ecosystem
51Nobel Prize winners in Chemistry and Medicine
affiliated to University of Cambridge
~61%Of start-ups with funding >$ 10m are in
Biotech / life sciences
~180Biotech R&D, and Pharmaceutical
manufacturing firms in Cambridge
2.95Total Science Patents per 10,000 working age
population, only behind Boston and San Diego
Major Science Parks in CambridgeBiotech/Pharma Start-ups have raised $16.4 B, more than 3 times the next biggest category, enterprise software
Cambridge Science Park
~40 St John’s Innovation Centre
~10
Cambridge Biomedical Campus
~20
Babraham Research Campus
~40
Top Company Activity
Abcam moved to a bigger facility in Cambridge Biomedical campus with plans to increase focus on new age tech areas
China based Novogeneestablished it’s first European genomic sequencing centre at Babraham Research Centre Molecular
Immunology
Therapeutics for rare diseases
Therapeutics in Oncology
Therapeutics in Oncology
Epigenetic Tools
Key Start-ups Area
~xx : No of biotech/ life science
companies in the Science Park
$448 M
AstraZeneca, MedImmune, One Nucleus, and RxCelerate launched a bio-incubator and life sciences accelarator at BabrahamResearch Campus
Note: 210 Start-ups founded after 2010 were analysed for this study
Note: The represented data has been analysed using DRAUP Proprietary Talent Database 29
30
Life Sciences : AstraZeneca and Illumina have high AI/ML talent pool while other MNCs have high Software development talent
1500-160070% 10% 0% 10%
Main focus is on using AI for drug discovery, synthesis of
molecules and imaging, Recurrent neural networks &
Reinforcement learning, Cognitive computing, IoT, AR,
VR and mixed reality technologies for Cardiovascular,
Oncology & Neuroscience applications
Human Longevity Inc.: 10 year deal – gene sequencing
200-25070% 20% 0%
Main focus is on AI based digital diagnostic solutions,
development of digital pathology using AI, networked digital
pathology, home healthcare solutions and disease
management
50 - 6025% 0% 50% 0%
5%
15%
Products/TechnologiesAI/MLComputer
Vision
Software
DeveloperSecurity UI/UX IoT
80 - 100
60 - 80
20 - 25
NLP
15%
Majority of engineering focus is on algorithm design,
application of deep/ML for health data analysis, gene
sequencing & diagnostic applications.
100,000 Genomes Project: Gene sequencing for cancer
patients and rare diseases
40 - 500% 0%
5 - 10
Main focus is on Drug development, global patient safety,
oncology, Haematology/ Oncology Bone & Neuroscience,
Bone and Metabolic Medicine
100 - 15095% 0%
Majority of focus is on cell and gene therapy discovery,
adoptive T-cell therapeutics, Specialist oral health and
translational medicine0%
20 - 30
0% 0%
0% 0%
5% 15%
5% 5%
0% 0%
0%
0%
0%0%
50% 50%
0% 0-25% 25%-50% >50% Engineering Talent Digital Talent
Note : DRAUP’s proprietary talent module was used to analyse jobs by top companies and skill type 30
31
Life Sciences : Talent Maturity Analysis - Over 50% of the employed digital talent in Security, Data Science and IOT Job families have 10+ years of experience
Employed Talent Distribution Across Job Roles and Years of Experience
HealthCare Talent~950Employed Talent is
(analyzed job roles)
29% 32% 33%
15%
33%
6%
33% 33%
21%21%
33%
20%
33%
16%
33% 33%
50% 47%
33%
65%
33%
74%
33% 33%
Data Science Software Engineer Cloud Security UI/UX IoT Computer Vision NLP
0-5 Years 5-10 Years 10+ Years
~250 ~350 <50 ~100 ~50 ~150 ~100 <50
Note: Talent size for NLP and computer are included in Data Science Job family
Embedded Job roles are included in analysis of IOT Job family
Note: The represented data has been analysed using DRAUP Proprietary Talent Database 31
32
Life Sciences: Technology Focus- Health Data Analytics, Gene Sequencing and Chronic Rare Diseases Management are the emerging capabilities in health care with high AI/ML innovation
INSIGHTS
Hiring Matrix
Function of the number of Current Job Openings and
Last 1 year hiring as percentage of Employed talent
Talent Maturity
Function of employed talent pool size
HighLow
High
Em
plo
ye
d T
ale
nt
Po
ol
Ind
ex
Hiring Matrix
Nascent CapabilitiesNiche Capabilities
Emerging CapabilitiesMature Capabilities
Genomics
Data
Analytics
Therapeutics
Gene
Sequencing
Medical
Robotics Medical
Imaging
Chatbots &
Voice
Interfaces
Remote
Health
Monitoring
Health Data
Analytics
Digital
Diagnostics
Connected
Medical
Devices
Virtual Drug
Development
3D Modelling
Chronic &
Rare Disease
Management
Prosthetics
Molecule
Design
Software
Drug
Discovery
S/W Dev.
Security
AI/ML
Cloud
Computer
Vision
IoT
UI/UX
Design
NLP
Astrazeneca is developing connected devices such as connected inhaler for Asthma patients and health monitoring systems.
Companies such as Astrazeneca and Cambridge Cognition are working on applications of AI/ML for Drug Discovery & Development, Digital Diagnostics and Therapeutics development
Illumina is leveraging AI/ML technologies for Gene Sequencing and Genomic Data Analysis
GSK used Luminoso’s NLP and text analytics technology to create a database and study patterns in patients’ concerns about vaccination so as to incentivize childhood vaccination.
Note: Analysis is based on the DRAUP’s proprietary engineering database32
Start-ups Analysis
33
Start-up Ecosystem : Cambridge is home to 210 funded Start-ups with an accumulative funding of $1Bn
Source: DRAUP Startups Module – Includes startups across major geographies such as US, Canada, Israel, Europe, China and India. Coverage may be limited in China and other south east APAC
regions. The list above is updated as of May, 2017
~60% of VC funds have been raised in last 3 years
30% YoY growth in number of start-ups since 2010
Highest Exits in Biotech & Enterprise Software
2010 2011 2012 2013 2014 2015 2016 2017 2018
$60 $73 $88
$137
$216
$432
$698
210129 159997347 19618427
YTD*
~$1Billion USD
Cumulative Funding
~$0.04Billion USD
No. of Start-upsx
34
35
Start-up Ecosystem : Life sciences/Biotech start-ups received ~47% of the total funding, major focus areas include - AI Imaging, Clinical NLP and Robotic Surgery
~$1 B
~210
• MA- Media & Advertising
• FT- FinTech
Okiki app enables movie viewers to connect with each other/celebrities via chat and live-streaming.
Fo
cu
s
Inte
nsit
y
LOW
HIGH
Clinical NLP
14%4%5%8%34%35%
Start-ups across sub-verticals
Home Automation
Augmented/Virtual
Reality
Autonomous Drone
Delivery
AI Image Classification
Robotic Surgery
UI/UX
Advertising Analytics
ML based Content
Delivery
AI/ML
Analytics
Digital Banking
Fraud/ Risk Analytics
Blockchain Technology
Software Development
Disruptors
DarkTrace uses AI and unsupervised ML to autonomously detect and fight cyber attacks across all digital environments
Use Cases
Kymab leverages AI Image classification for Antibody discovery, vaccines, & Innovative Antibodies
SyndicateRoom’s unique Algorithm draws on data from the investment decisions of lakhs of sophisticated investors to determine which companies to invest in.
5%4%4%2%47%38%
Software/ Internet Healthcare MA Telecom FT Others
Funding
Start-up Count
Focal Point Positioning developed a next generation navigation & positioning software for smartphones, wearables & autonomous vehicles leveraging IoT
Focal Point
Positioning
Note: Data curated by DRAUP Start-up module and updated in Nov 2018
Note: The list above is non-exhaustive
35
Source: DRAUP Startups Module – Includes startups across major geographies such as US, Canada, Israel, Europe, China and India. Coverage may be limited in China and
other south east APAC regions. The list above is updated as of May, 2017
Cloud
AI/ML
Computer
Vision
IoT
Software
Technology Talent Pool Unicorns Roles and Skills Scale
~100
• Data Scientist
• ML Engineer
• Applied Scientist
• Cloud Engineer
• Cloud Enterprise Architect
• Cloud Infrastructure Engineer
• Cloud Architecture Engineer
UI/UX
~200
~100
~1300
~3000
~2500
• Embedded Software Engineer
• Principal System Engineer
• Senior DSP Engineer
• Software Lead Engineer
• Software Engineer
• Full Stack Developer
• Senior Software Developer
• Web Developer
• Frontend Engineer
• UI Designer
• UX Developer
• Interaction Engineer
• Computer Vision Engineer
• Computer Vision Research Engineer
• Senior Computer Vision Engineer
• Computer Vision Technologist
~16%
~4%
~6%
~10%
~5%
~5%~4%
~13%
~10%~8%
~5%
~5%
~2%~1%
~3%
~5%
~1%~2%
~1%
~1%
~2%~1%
~1%
Lateral Hiring Targets: MNCs can target a host of scaled start-ups to leverage their mature talent pool
36
Top Patent-filing Start-ups
Industry Patent Use Case
Semiconductors• Flexible fingerprint sensors• Flexible OLED displays• Glass-free organic LCD
Life Sciences & Biotech
• Portable surgical robotic systems for minimal access surgery (endoscopy & laparoscopy)
Enterprise Software- AI/ML
• Network security using AI in both IT & OT environments
• Anomaly alert system for cyber threat detection using in-house ML techniques
Enterprise Software- Security
• Data protection in Trustonic products’ (TEE or TAP) technology implementations
• Security system for Kinibi based devices providing REE state monitoring
Enterprise Software- Security
• Device security using genome data
Enterprise Software- IoT
• Communicating between applications, running on different nodes, having logic in differing languages to monitor and control multiple devices with one unified interface
Industry-wise Patents filed by Cambridge Start-ups
~90% of Total
patents files
4
5
0
1
6
44
3
3
9
21
73
53
Energy &Environment
Aviation &Aerospace
Chemicals
Enterprise Software
LifeSciences &Biotech
Semiconductors
Granted Pending
Patents filed by
Cambridge Start-ups in UK222Between 2010 & 2018
Patents: Semiconductors, Life sciences/Biotech and Software/Internet Startups filed ~90% of all start-up patents
Note: The represented data has been derived using DRAUP Proprietary Talent Database37
38
Start-up case study: Darktrace and GeoSpock
Source : ZinnovSource : ZinnovSource : Zinnov
Leadership
Darktrace Antigena autonomously interrupted
a ransomware attack at a telecommunications
firm. It detected a suspicious SMB encryption
activity within 9s and stopped the attack by
revising its understanding of deviation
Uses AI and unsupervised ML to autonomously detect and fight cyber attacks across all digital environmentsEnterprise Immune System: Passively analyses raw network traffic to independently learn to
detect significant deviations and alert the organization to emerging threats.
Darktrace Industrial: Detects cyber threats by monitoring network traffic across both OT and IT to
conceive a normal pattern for the user/system and identifying potential threat at an early stage
Products
Funding $229.5 M
Investors
Employees ~750
DAVE PALMERDIRECTOR OF TECHNOLOGY
POPPY GUSTAFSSONCO-CEO
NICOLE EAGANCEO
Funding $17.5 M Employees ~50
Case Study
Top Job Roles Work Load
• Leverage machine learning and artificial intelligence for the detection of and response to network anomalies
Cyber Security (MachineLearning ) Analyst
Leadership
Geospock suite can help in harnessing data of time and location used in designing strategies for smart cities like trend/correlation analysis, prediction models, evidence-based decisions, etc
Help in handling extreme-scale data in real-time using big data engineering infini8: Used for data indexing to preserve large scale data in its propriety data storage solution
extrapol8: Enables both general and specialist data analytics
Illumin8: Used for data visualization for geospatial analytics
Products
InvestorsCase Study
Top Job Roles Job Load
RICHARD BAKERCEO
STEVE MARSHFOUNDER, CTO
IAN HAMMONDCOO
~ 55
Software Developer ~ 20• Designing and implementing web applications• Conducting code-reviews and end-to-end testing• Architecture design and focusing on clean code/design usage
Cyber Security Engineer ~ 10• Work on-site with the client to develop internal cyber security systems for
them• Build custom solutions and provide troubleshooting services
• Identifying, analyzing and determining the root cause of Advanced Persistent Threats, network anomalies and unusual activity within a client network
CyberDefense Specialist ~ 30
• Develop high-performance visualizations of very large scale geo-temporal data• write scalable and well-engineered code using JavaScript, React, HTML, CSS,
GraphQL and WebGLSoftware Engineer/Developer ~ 15
• Analyze large-scale heterogeneous datasets using data modeling, software development, statistics, data interpretation, machine learning and data visualization
Data Scientist ~ 2
• Analyze data in petabyte-scale to build predict analytics models• Use latest tools and techniques in the ML/AI space combined with GeoSpocks
data processing technology• Co-ordinate with internal platform and infrastructure teams to get algorithms
running at extreme scale
Machine Learning Engineer ~ 2
Note: The represented data has been derived using DRAUP Proprietary Start-up Database 38
39
Start-up case study: Kymab and Mission Therapeutics
Source : Zinnov
Leadership
Granted Novo Nordisk licence toKymouse™ with access to transgenichuman antibody mouse strains, togenerate highly selective, potent,human antibody drugs
Therapeutic Antibody: Captures diversity of the B lymphocyte component of immune system and generates antibody-based biopharmaceuticalsVaccines: Immunogen discovered through Kymouse™ elicits a protective immune response to a given pathogen to treat the disease
Products
Funding $230M
Source : Zinnov39
Investors
Employees ~ 200
David ChiswellCEO
Allan BradleyFounder
Arndt SchotteliusExec. VP
Funding $128M Employees ~50
Case Study
Top Job Roles Job Load
Support pharmacology scientists with in vivo and ex vivo work. Research Associate
Leadership
Collaboration in the research andpreclinical development of specifiedDUB inhibitors for the treatment ofAlzheimer’s Disease and Parkinson’sDisease
Chemistry Platforms: A customizable platform with novel chemistries for DUB-targeted therapeutics to enhance target selectivity and drug potencyPatents: Mission is protecting its platform and pipeline with many patent filings covering target validation, proprietary assay development
Products
InvestorsCase Study
Top Job Roles Job Load
Dr. Anker Lundemose CEO
Prof. Steve JacksonFounder
Dr. Paul WallaceCBO
~ 60
Research Scientist ~ 40
Biomarker Developer ~ 10
Formulation and characterization of nanoparticles Drug with experience in Immuno-oncology
Contribute scientifically to inception and realization of biomarker discovery and development
Bioinformatician ~ 5Application of data analysis and machine learning methods to biological and medical product development.
Develop & support projects in biochemistry/molecular biology, such as assay development & protein production
Research Scientist ~ 20
Drug development from target validation to pre-clinical with expertise across range of in-vitro and assay validation.
Principal Scientist
Responsible for all aspects of pharmaceutical IP for in-house patent department
Patent Attorney
Discovery and drug development in metabolic disease with focus on diabetes, cardiovascular disease and obesity.
R&D Project Manager
~ 10
~ 5
~ 5
Note: The represented data has been derived using DRAUP Proprietary Start-up Database39
40
Start-up acquisition analysis: Acquisitions – Majority of acquisitions are happening in AI/ML and Security segments
Technology and market expansion are primary drivers for M&A
0% 5% 10% 15% 20% 25% 30%
Big-Data Analytics & BI
ArtificialIntelligence/ML/Computer
Vision/NLP
Embedded Systems/IoT
Software & UI/UX Development
Security
Cloud Technology
Virtual Reality & 3D
Acquisitions of ~100 start-ups by Technology Type
Start-up count by %
Apple acq. VocalIQ
$70M
Acquisition Driver
Apple acquired Vocal IQ , a University of Cambridge spin-out. Objective of theacquisition was to enhance the capability of speech recognition technologyfor Siri , as Vocal IQ had created the world's first self-learning dialogue API.
Bolster Technology Stack
Amazon acquired Evi Ltd, a technology based company focused on knowledge systems and semantic search engine. This addition propels Amazon’s current offering of its voice-driven AI assistant Alexa.
Enter New Markets
Amazon acq. Evi Ltd
British Gas acq. AlertMe
British Gas acquired AlertMe, a Home-based IoT devices company. The purchase will help British Gas with an opportunity to be the leader in intelligent connected home services.
Acquihire Talent
$100M
Huawei Tech. acq. Neul
Huawei acquired Neul Ltd, a IoT based connectivity firm with the aim to bring new Internet of Everything applicative products into its portfolio for EU region
Build New Products$25M
$26M
Source: DRAUP Proprietary: SWARM Disruption Framework for Start-up Analysis40
University Analysis
41
42
University Ecosystem : 6 universities supply a fresh talent of ~9000 IT/Computer engineering talent annually; majority of software talent graduated, contribute to Life science industry
• Oxford, Cambridge and London
(Golden triangle) houses the UK’s
largest biomedical cluster with
around 170 Medical biotechnical
companies linked to universities
• Universities of the Golden Triangle
work collaboratively with initiatives
like G5, Global Medical Cluster,
MedCity, and SES, to ensure that
the Golden Triangle becomes a
global science and innovation hub
• Latest 'AI and Big data' courses at
competitive enrolment fee attracts
UG and PG talent from across the
globe, paving the way for
fundamental research into data
analysis and machine learning with
applications in Computer science,
Medicine and Technology
Universities/ colleges Landscape in Golden triangle
Most common courses pursued
• Biotechnology
• Computer Science
• Information technology and Telecommunications
35%
27%
24%
9%
5%
Life Science Humanities Computer &Engineering
History &Arts Economics
Course Adoption trend
Major Universities in and around Cambridge 6
Software/Computer graduates from the 6
universities~9k
Software/Computer graduates from
universities in Cambridge ~900
Total Graduates ~60k
Contribute to software talent
Note : DRAUP’s Talent Module analysed universities in Cambridge and nearby locations to identify top universities and key courses
Top Universities/ colleges in Golden triangle
42
43
University Ecosystem : Maturity of courses offered by top universities/colleges
Top AI/ Big Data Talent Universities Beginner Courses Intermediate CoursesAdvanced
Courses
UNIVERSITY OF CAMBRIDGEJava & Object-Oriented Programming, Operating
Systems, Discrete Mathematics & Algorithms,Human-Machine Interaction
Machine Learning and Real-world Data, Artificial Intelligence, Concurrent and Distributed Systems,
Software and Security Engineering,
Mobile Robot Systems, ML and Bayesian Inference, Multicore Semantics and Programming, NLP,
Computer Vision, Cloud Computing, Data SciencePractices
ANGLIA RUSKIN UNIVERSITYComputer Gaming Technology, Cyber Security,
Artificial Intelligence (Basic), Basic Maths for Technology, Computing and Information Systems
Core Mathematics for Computing, Network Routing and Switching Essentials, Digital Security, Digital Data
Storage and Transmission
Network simulation and modelling, Internet Services, Data Analytics and the Cloud, Image Processing, Data
Structures and Algorithms
UNIVERSITY OF OXFORDFunctional & Imperative Programming, Linear
Algebra, Probability, Design and analysis of algorithms, Discrete mathematics
Concurrent programming, Intelligent systems, Machine learning, Geometric modelling, Lambda
calculus and types
Probabilistic model checking, Probability and computing, Advanced Security, Quantum Computer
Science, Natural Language Processing
KING’S COLLEGE, LONDONProgramming Practice & Applications, Computational & Mathematical Thinking, Statistics for Data Analysis,
Computer Systems
Practical Experiences of Programming, Introduction to Artificial Intelligence, Internet Systems, Data
Structures, Data Mining
Artificial Intelligence Reasoning & Decision Making, Cryptography, Natural Language Processing, Artificial
Intelligence Planning
IMPERIAL COLLEGE LONDONComputer Architecture, Computational Optimisation, Mathematics for Machine Learning, IoT, Information
and Coding theory
Visual Computing and Robotics, Computer Vision, Statistical Machine Learning and Pattern Recognition
Dynamical Systems and Deep Learning, Advanced Security, Computer Vision, Advanced databases,
Quantum Computing
UNIVERSITY COLLEGE LONDONTheory of Computation, Principles of Programming,
Logic and Database Theory, Digital Security
Networking and ConcurrencySecurity, Database and Information Management
Systems, Data-Mining
Artificial Intelligence and Neural Computing, Crypto Analysis, Computer Vision, NLP
Courses offered by Top Universities/Colleges
Note : DRAUP’s proprietary talent module was used to analyse millennial moments across various cities
Cambridge based universities Universities outside Cambridge
43
44
Total Enrolment:
~20,000
Total Undergraduates:
~12,000Computer Science:
~300
Total Post Graduates:
~7,200Computer Science:
~315
45+ Courses
Tech Collaboration & COEs University CollaborationKey Startups Born Venture funds /Accelerators
Collaboration
University of Cambridge : Leading tech giants such as Google, Microsoft, and Huawei have collaborated with University of Cambridge for research in the field of AI, and Deep Learning
Note : DRAUP’s Talent Module analysed 100,000+ global universities to identify top universities and key courses in software engineering, ML and Big Data
VENTURES• 250+ firms founded by Computer Lab
Alumni• ~200 Ventures at Accelerate
Cambridge• >60% of ventures are based on AI/ML
new Technologies.• ~100 Ventures Working in
Lifesciences/biotech
ACCELERATE CAMBRIDGE PROGRAMTo enable and Nurture venture creation out of the university of Cambridge
Microsoft Research has
collaborated with University
of Cambridge in Machine
Learning to provide support
for Ph.D. students & also
offers positions at Microsoft Research Lab
University of Cambridge, BT
and Huawei have announced
a new five-year initiative to
establish a joint research and
collaboration group at the
University of Cambridge.
CU launched a DeepMind
Chair of Machine Learning
to build on a wide range of
AI related research.
ImprobableRaised $ 502 M from
SoftBank
PROWLER.ioRaised $ 13 M from
Cambridge Innovation Capital
VocaliqAcquired by Apple
University of Cambridge collaborated
with MIT for projects such as Aesthetic
Imaging, Emotionally Intelligent
Surfaces etc
University of Cambridge collaborated with
USC for projects related to Computational
Aesthetics, Computing & Intelligent
Interaction etc
University of Cambridge collaborated
with IIT Bombay for ongoing research on
Nanotechnology with focus on enhancing
micro-scale precision sensing
technology.
$9 Mn
$1.9 Mn $5.5 Mn
44
University Collaborations: Tech giants have majorly collaborated with University of Cambridge for co-innovation, skill enhancement and setting up of digital labs/research centres
Note : The above analysis is based on the DRAUP’s proprietary engineering database and insights from industry stakeholders, updated as on Aug, 2017
Microsoft and University of Cambridge took a strategic step to
modernize engineering curriculum by providing Azure
notebooks to the Students
Microsoft AI Residency program is a collaborative coursework
with University of Cambridge which bolsters application of AI in
the fields of Healthcare, Biotech and Education
Skill Enhancement
Digital Lab/Centre Co - Innovation
Investment/Deal
Low High
Corporate Collaborations
Nokia and University of Cambridge partnered on joint research
projects on Nanotechnology at Nanoscience Centre and
Electrical Engineering Division
Research agreement is signed between University of Cambridge
and AstraZeneca to lower the engagement and regulatory
barriers and speed up the research
The University of Cambridge, BT and Huawei signed a new five-
year initiative which focus photonics, digital and access network
infrastructure and media technologies
HP signed $210 million agreement with Cambridge University
Hospitals to create a secure, robust, integrated IT environment
that allows clinical staff to access a unified view of each patient’s
clinical and administrative information via its new electronic
patient record system.
Nokia Bell Labs is a founding partner of the new Centre for
Mobile, Wearable Systems and AI, to be based in University of
Cambridge
AstraZeneca is currently working with Microsoft and the
University of Cambridge at IMED Biotech Unit to work in cancer
research more effectively
GSK has its only trials unit in UK in the Cambridge Clinical Trials
Unit which has the long-term ambition of jointly delivering new
medicines to patients in the next five to ten years
Established the Omeros Center at Cambridge for Complement
and Inflammation Research for wide range of diseases including
thrombotic microangiopathies, kidney diseases and central
nervous system disorders
Boost drug formulation. It helps patients to more easily absorb
poorly soluble drugs administered orally via tablets and capsules
Intensity showing number of corporate collaborations
45
46
46
University of Cambridge : University of Cambridge has 60+ professors/lecturers focusing on new age digital technologies
Department of Computing and Technology Research (60+ Faculty members)
Artificial
Intelligence
Group
5 Faculty Members
2 Researchers
18 Students
Research Focus Genomics and
Bio- Informatics,
Computational
Theory,
Computer Vision,
Cognitive Science
Computer
Architecture
Group
7 Faculty Members
10 Researchers
12 Students
Research Focus Languages and
compilers for
multi-core
architectures,
Processor
Architecture,
Programmable
processing
substrates,
Resilient cloud
computing
Digital
Technology
Group
14 Faculty Members
13 Researchers
18 Students
Research FocusLow-Power
Microprocessor
Design,
Channel Coding
and Signal
Processing for
Wireless
communications,
Wireless Sensor
Networks,
Cross-Layer
Wireless Access
Graphics and
Interaction Group
5 Faculty Members
8 Researchers
9 Students
Research FocusGraphics and
imaging,
Interaction and
design,
Affective
computing,
Stereoscopic
displays,
Aesthetic imaging,
Personal projected
displays,
Inclusive user
interfaces
NLP Group
6 Faculty Members
8 Researchers
24 Students
Research FocusSpatial & Personal
Adaptive
Communication
Environment,
Feedback for User
Adaptive Statistical
Translation,
Computational
Natural Language
Processing and the
Neuro-Cognition of
Language
Programming
logics &
Semantics
14 Faculty Members
6 Researchers
14 Students
Research FocusProgramming
language design,
Development of
interactive theorem
provers,
Development of
automatic proof
procedures, Formal
verification of
computational
systems, semantic
models using
techniques such as
structural
operational
semantics
Security Group
7 Faculty Members
11 Researchers
11 Students
Research FocusBanking security,
Biometric
identification,
Microcontroller
security ,
Robustness of
cryptographic
protocols,
Quantum
Cryptography,
Security of Clinical
Information
Systems,
Information hiding
Systems
Research Group
10 Faculty Members
9 Researchers
22 Students
Research Focus On Content
Indexing for Off-
Path Caching in
Information-Centric
Networks ,
Scalable Provision
of Semantically
Relevant Web
Content on Big
Data Framework ,
Hybrid Renewable
Energy Routing for
ISP Networks,
Blockchain-enabled
Wireless Mesh
Networks
Reference:1. Faculty Members – Professor / Lecturer2. Researcher – Post Doctorate Researcher3. Student – PhD Students
Systems
Research Group
Note : The above information is based on data provided by University of Cambridge and DRAUP Proprietary Database
47
University of Cambridge - Professor Profile
Research Works
Student Profile
Post doc PhD Others
Jose Miguel Hernandez LobatoUniversity Lecturer in Machine
Learning, Department of
Engineering
University of Cambridge
Email: jmh233-at-cam.ac.uk
Phone: +44 (0) 1223 738 513
Design and implementation of scalable methods
for approximate inference and construction,
evaluation and refinement of probabilistic models
that describe the statistical patterns present in the
data
Research Highlights
Research Goal:Design of model based machine learning which focuses on
probabilistic learning techniques on Bayesian optimization, matrix
factorization methods, copulas, Gaussian processes and sparse
linear models
Reported Topics: A General Framework for Constrained Bayesian Optimization using
Information-based search
Expectation Propagation in Linear Regression Models with Spike and slab
Priors for Machine Learning
Development at Lab
Model-based machine learning: designing
machine learning algorithms that are
specifically tailored to each new application
Bayesian framework: Variables in the
probabilistic model areencoded using
probability distributions. Bayes’ theorem is then
used to combine these probability distributions
with the observed data
Uncertainty Decomposotion in Bayesian
Learning with Deep Generative Models
Publications
~10
Ongoing research initiatives: Implementation of scalable methods for
approximate inference, construction, evaluation
and refinement of probabilistic models
Statistical patterns for data in Bayesian Machine
Learning
B.Sc. in Computer Science, Autonomous University of Madrid
M.Sc. in Computer Science, Autonomous University of Madrid
Ph.D. in Autonomous University of Madrid
Education
Student Research Support
Ross Clarke
PhD Student, University of Cambridge
• Suárez A: Linear Regression Models with Spike-and-slab Priors
• Depeweg S: . Decomposition of Uncertainty in Bayesian Deep Learning
for Efficient and Risk-sensitive Learning
Research Supported / Co-Authored:
NA 5 6
Source : The above information is based on data provided by University of Cambridge
Note : DRAUP has analyzed top professor profiles based on their number of publications and ongoing research initiatives
47
48
University of Cambridge - Professor Profile :
Research Works
Student Profile
Post doc PhD Others
Alastair Beresford
Senior Lecturer
Department of Computer Science
University of Cambridge
Email: arb33 at cam.ac.uk
Phone: +44 1223 763597
Designing and building novel prototype
technologies, by measuring the human behaviour
that focuses on networked mobile devices, such
as smartphones, tablets and laptops
Research Highlights
Research Goal:Examine the security and privacy of large-scale distributed computer
systems and networked mobile devices, such as smartphones, tablets
and laptops.
Examine the security of devices for the security and privacy problems
induced by the interaction between mobile devices and cloud-based
Internet services
Reported Topics: TIME-EACM project - Explored how sensor networks and distributed systems
can be used to improve traffic and transport
Cambridge Mobile Urban Sensing Project - measured and monitored air
quality, particularly urban pollution generated by motor vehicles
Development at Lab
Nigori: storing secrets in the cloud: build
a secure mechanism for storing sensitive user
data on servers connected to the Internet
tailored to each new application
STRIDE: improve the delivery of real-time and
historic transport and traffic data
Publications
Ongoing research initiatives: TRVE Data
Cambridge Cybercrime Center
Device Analyzer
Isaac Physics Platform
Wearable Systems and Augmented Intelligence
B.Sc. in Computer Science, University of Cambridge
Ph.D. in Department of Engineering, University of Cambridge
Education
Student Research Support
Stan Zhang
PhD Student, University of Cambridge
• Martin Kleppmann: Secure Messaging to Secure Collaboration
• Victor B.F. Gomes: Verifying strong eventual consistency in distributed
systems
Research Supported / Co-Authored:
NA 4 6Journals Others
1252
Source : The above information is based on data provided by University of Cambridge
Note : DRAUP has analyzed top professor profiles based on their number of publications and ongoing research initiatives
48
49
University of Cambridge - Professor Profile :
Research Works
Student Profile
Post doc PhD Others
Dr Sean B Holden
Senior Lecturer
Department of Computer Science
University of Cambridge
Email:sbh11 at cl.cam.ac.uk.xx
Phone: : +44 (0)1223 763725
Analysis of error estimation techniques within a
framework based on Probably Approximately
Correct (PAC) learning
Research Highlights
Research Goal:Design and underline the links between bounds and combine the
improvements into a single bound. Combine the PAC-Bayes
approach for randomized predictions with the optimal union bound
using generic chaining technique for variance of the combined
functions
Reported Topics: Support Vector Machines for QSAR - designed a single compound to search
chemical space for elimination of compounds with desirable properties such
as toxicity or "drug-likeness"
Quantum computation applied to machine learning - performed computations
considered intractable (in the formal sense) for any standard (non-quantum)
computer
Software Development at Lab
HasGP - Gaussian Processes in Haskell-Implementation of Gaussian process for
regression and classification based on the
treatment
Bayesian Hierarchical Ordinal
Regression – Matlab code for the material in
paper: "Bayesian Hierarchical Ordinal
Regression”, proceedings of the International
Conference on Artificial Neural Networks
The Generalized FITC Approximation -Matlab code for material in the paper: "The
Generalized FITC Approximation”, proceedings
of Neural Information Processing Systems
Publications
~32
Ongoing research initiatives: Theoretical models for supervised learning –
aims to prove bounds on the performance of
learning systems
Bayesian inference
B.Sc. in Electronic Systems Engineering, University of East
Anglia
M.Sc. in Intelligent Systems, University College London
Ph.D. in Cambridge University
Education
Student Research Support
Nicholas Pilkington
PhD Candidate, University of Cambridge
• Richard Russell: Planning with preferences using maximum satisfiability
• Andrew Naish-Guzmann: Thesis Sparse and Robust Kernel Methods
• Ulrich Paquet: Bayesian Inference for Latent Variable Models
Research Supported / Co-Authored:
1 NA NA
Source : The above information is based on data provided by University of Cambridge
Note : DRAUP has analyzed top professor profiles based on their number of publications and ongoing research initiatives
49
50
University of Cambridge - Professor Profile :
Research Works
Student Profile
Post doc PhD Others
Hatice GunesSenior Lecturer
Department of Computer Science
University of Cambridge
Email:
Hatice.Gunes(@)cl.cam.ac.uk
Phone: NA
The Bimodal Face and Body Gesture Database
(FABO) for Automatic Analysis of Human
Nonverbal Affective Behavior to combine face
and body, enabling significant future progresses
in affective computing research
Research Highlights
Research Goal:Focus on Digital Personhood through the ‘EPSRC Being There’
Project that aims to produce greater social integration of robots in
public spaces, and to increase access to public spaces in robot proxy
forms.
Reported Topics: Applied machine learning
Computer vision
Human-robot interaction
Social signal processing
Social robotics
Artificial Emotional Intelligence
Software Development at Lab
Quantised Local Zernike Moments
(QLZM) - Challenge visual feature extraction
for affect recognition on the AVEC'12 Dataset
Probabilistic Subpixel Temporal
Registration (PSTR) – Probabilistic Subpixel
Temporal Registration for Facial Expression
Analysis
Publications
Ongoing research initiatives: The SEMAINE: aims to build a SAL, a Sensitive
Artificial Listener, a multimodal dialogue system
Building a multi-modal/cue module: extract
features from expressive face and upper-body
gestures using computer vision and image
processing techniques
B.S. in Computing Science, Yildiz Technical University
Ph.D. in Computing Science, University of Technology,
Sydney
PGCAP, Queen Mary, University of London
Education
Student Research Support
Wenxuan Mou
PhD Candidate, University of Cambridge
• E. Skordos: Multimodal Human-Human-Robot Interactions (MHHRI)
Dataset for Studying Personality and Engagement
• M. Pantic: Continuous Prediction of Spontaneous Affect from Multiple
Cues and Modalities in Valence-Arousal Space
Research Supported / Co-Authored:
1 3 NA
11 50+22
Journals BookChapters
Others
Source : The above information is based on data provided by University of Cambridge
Note : DRAUP has analyzed top professor profiles based on their number of publications and ongoing research initiatives
50
51
Anglia Ruskin
University
Total Enrolment
~22,500
Total
Undergraduates:
~18,000
Total
Post Graduates:
~4,000
Total Researchers:
100+ post doctoral
researchers in CS
University CollaborationsKey Startups Born
(Primarily Technical)Tech Collaboration & COEs
Anglia Ruskin University offers Cisco Certified courses which give you
valuable information and communication technology skills to enhance
student-skillset. Also develop IT networking skills for students and preparing
them for the CCNA certification.
The university has been a Cisco partner since 1999 and trained over 100
instructors and 1000 students
REACTOR
• EU-funded regional development project.
• Growth of applied games sector in the Cambridge shire/Peterborough
region
Anglia Ruskin University: Leading tech giants such as Google, Microsoft, and Huawei have collaborated with University of Cambridge for research in the field of AI, and Deep Learning
PureChimpStartup to help people
with various Skin
Problems
ACTING NOWSocial theatre Company
Uni CompareSocial comparison site
for universities across
the UK
ARU is tied up with Global Education
Institutes. It has varied partnerships,
collaborative programs based on the type of
courses & regions:
1. London (2) & UK (9) Partnerships like
o London School of Osteopathy
o University Centre Peterborough
2. International (14) Partnerships
o Indian School of Business and
Computing - Bengaluru, India
o Budapest Business School - Budapest,
Hungary
3. Distance Learning (4) Partnerships
o Resource Development International
o CNET training
Exemplary Programs
• KEEPs (Knowledge Exchange and Embed
Partnerships)
• Innovation Accelerator Partnerships
• In Neuroscience and Vascular Simulation
established a diverse and collaborative
environment for conducting research.
Note : The above information is based on data provided by Anglia Ruskin University and DRAUP Proprietary Talent Database 51
52
52
Anglia Ruskin University: The University has 20+ professors/lecturers focusing on new age digital technologies
Department of Computing and Technology Research (20+ Faculty members)
Anglia Ruskin IT Research
Institute
15 Faculty Members
8 Visiting Professors
6 Students
Research Focus
• Mobile enabled point of care
solutions to detect
tuberculosis, capture and
analyze images for diagnostics
• Development of IT solutions
for better connectivity
Cyber Security, Networking and
Big Data Research Group
3 Faculty Members
4 Students
Research Focus
• Cyber-based warning systems,
Autonomous threat detection
and threat intelligence, and
neutralization of network-
based attacks
• Next generation software-
defined infrastructure
• Application of machine
learning, data mining and
software defined networks to
make cyber threat big data
more manageable in areas
such as Smart Cities, IoT &
ICS
Informatics, Computing and
Electronics Research
5 Faculty Members
2 Visiting Professors
14 Students
Research Focus
• IOT and cloud computing
• Digital Electronics – SoC,
Embedded systems, FPGA
design, intelligent control, data
fusion
• Graphics, Imaging and Vision
• Image processing and Data
visualization
• Web Technologies, Intelligent
search and Knowledge
modelling
Sound and Game Engineering
and Research
9 Faculty Members -
4 University Professors
5 Lecturers
Research Focus
• Sound synthesis, sound
design, physical and
mathematical modelling,
analogue and digital
synthesisers, microcontrolled
platforms and Internet of
Things for gaming and sound
engineering, digital and real-
world game-based learning,
assisted navigation, virtual
reality and immersive systems,
ultra-portable audio/video
performance system
Reference:1. Faculty Members – Professor / Lecturer2. Student – PhD Students
Note : The above information is based on data provided by Anglia Ruskin University and DRAUP Proprietary Talent Database
53
Anglia Ruskin University - Professor Profile
Research Works
Student Profile
Post doc PhD Others
Dr Alin Tisan
Senior Lecturer
Department of Computer Science
Anglia Ruskin University
Email:[email protected]
Phone: NA
Design of modern sensors and communication
technology, with the advanced electronic devices
such as Field-Programmable Gate Array (FPGA),
algorithms and computer models and Artificial
Intelligence
Research Highlights
Research Goal:Designing AI algorithms in hardware (FPGA) using a developed
integrated hardware-software environment that combines complex
sensors, advanced Electronic Design Automation (EDA) tools and
hardware platforms
Reported Topics: ANN design and FPGA implementation using Matlab/ISE environment
Designing a new infrared temperature sensor
Development at Lab
Managing (processing) acquired data on
hardware and software platforms capable of
hosting different data reduction and artificial
intelligence algorithms
Neuromorphic hardware and system-on-
chip (FPGA) design
Electronic Design Automation (EDA) tools and
hardware platforms
Publications
7
Ongoing research initiatives: Artificial intelligence hardware (FPGA)
implementable
Telemedicine
Smart sensors
Holistic modelling of combined renewable
energy sources
B.S. in Engineering Physics, Babes-Bolyai University
M.S. in Physics, Babes-Bolyai University
PhD in Electronic Engineering, Technical University of Cluj
Education
Student Research Support
John Darvill
Ph.D. in Computing, Anglia Ruskin University
• Chin, J: End User platform for implementing Artificial Neutron Networks on
FPGA
• Cirstea, M: SOM neural network design – A new Simulink library based
approach targeting FPGA implementation
Research Supported / Co-Authored:
NA 2 1
Source : The above information is based on data provided by Anglia Ruskin University
Note : DRAUP has analyzed top professor profiles based on their number of publications and ongoing research initiatives
53
54
Anglia Ruskin University - Professor Profile
Research Works
Student Profile
Post doc PhD Others
Jeannette Chin
Senior Lecturer
Department of Computer Science
Anglia Ruskin University
Email:[email protected]
Phone: NA
Development of end-user programming for digital
home environments to increase the range of
services available from network and networked
devices
Research Highlights
Research Goal:End user programming and machine learning with analytics, and
applications within personalised services, smart or assisted living,
leveraging everyday artefacts and connectivity (IoT)
Reported Topics: Machine learning algorithms
Big data and analytics
End user programming and human machine research
Development at Lab
Publications
Ongoing research initiatives: Machine learning and personalisation
IoT and resilient systems
Big data analytics and semantic ontology
Intelligent environments, digital ecosystems and
assisted living
Human and machine research
B.S. in Internet Computing, University of Essex
PhD in Pervasive Interacting – Human Computer Interactions
Computing, University of Essex
Education
Student Research Support
Not Available• V. Callaghan: Understanding and personalising smart city services using
machine learning, The Internet-of-Things and Big Data
• Tisan. A: IoT-based pervasive body hydration tracker (PHT)
Research Supported / Co-Authored:
NA NA NA
5 296
Journals Book
Chapters
Others
Web Appliance- newly emerging embedded
Internet
iCampus: Pervasive-interactive-
Programming (PiP) paradigm
Machine research, digital ecosystems, signals
processing, analytics
Source : The above information is based on data provided by Anglia Ruskin University
Note : DRAUP has analyzed top professor profiles based on their number of publications and ongoing research initiatives
54
55
Anglia Ruskin University - Professor Profile
Research Works
Student Profile
Post doc PhD Others
Alagmir Hossain
Professor
Department of Computer Science
Anglia Ruskin University
Email:
Phone: NA
Developing a mobile enabled Point-of-Care
(POC) platform to detect TB and to improve
efforts in controlling TB through wider, faster and
low cost 24/7 real-time access
Research Highlights
Research Goal:Use phone’s camera to capture the sample, rather than manually
using colour charts, to eliminate human error and avoid any
subjectivity around interpretation for Tuberculosis diagnosis
Reported Topics: Intelligent Systems and Expert Systems using Artificial Intelligence
Mobile Enabled Expert Systems for Diagnosis
Cyber Security with a particular focus to Phishing
Big Data with a particular focus to Real-time and Optimal Solutions
Development at Lab
Publications
Ongoing research initiatives: Intelligent human-like behaviours in non-player
characters in games
Cyber threat analysis, prediction and detection
in online social networking
Behaviour-based malware detection using
machine learning techniques
B.S. Computer Science
Ph.D. in Automatic control & Systems Engineering, University
of Sheffield
Education
Student Research Support
Anjum Shaikh
PhD Candidate, Anglia Ruskin University
• Bourouis. M: Intelligent Mobile based Decision Support System for Retinal
Disease Diagnosis
• Neoh. S: Intelligent facial emotion recognition using a layered encoding
cascade optimization model
Research Supported / Co-Authored:
NA 6 NA
265
Journals Others
Artificial Intelligence and decision support
system
Intelligent Mobile-Based Diagnosis System
Artificial Intelligence and Neural Network
Mobile phone app to diagnose TB
Source : The above information is based on data provided by Anglia Ruskin University
Note : DRAUP has analyzed top professor profiles based on their number of publications and ongoing research initiatives
55
56
Anglia Ruskin University - Professor Profile
Research Works
Student Profile
Post doc PhD Others
Khin LwinSenior LecturerDepartment of Computer ScienceAnglia Ruskin UniversityEmail:[email protected]: NA
Development of real-time intelligent decision
support and knowledge management schemes
for data-intensive applications particularly in
healthcare, transportation, smart cities and cyber
security
Research Highlights
Research Goal:Focus on operational research and artificial intelligence for
applications of modelling, search and optimization techniques to
tackle constrained combinatorial problems to underpin the
development of intelligent decision support systems across a wide
range of real-world applications in healthcare, transportation, smart
cities and cyber security
Reported Topics: Big Data Analytics
Machine Learning and Data Mining
Innovative IoT’s and Smart Cities Applications
Intelligent Decision Support System
Portfolio Optimization
Multi-objective Optimization
Cyber Security
Development at Lab
Publications
Ongoing research initiatives: Intelligent phishing detection in real-time
transaction
An intelligent mobile-enabled expert system for
tuberculosis disease diagnosis in real time
BSc (Hons) in Computer Science, University of Nottingham
PhD in Computer Science, University of Nottingham
Education
Student Research Support
• Qu.R: A hybrid algorithm for constrained portfolio selection problems,
Applied Intelligence
• Sabor.M: New Social Engineering Challenges in Phishing – A Case Study
of Ransomware Attack, Cybersecurity
Research Supported / Co-Authored:
NA 3 NAJournals Others
Big Data & Cyber Security – Manipulate, use,
store and exploit huge amounts of data using
data mining, classification and clustering
methods
Intelligent modelling and Big Data mapping
for the analysis of connectivity and
regeneration
Marzia Hoque Tania
PhD in Artificial intelligence,
Anglia Ruskin University
114
Source : The above information is based on data provided by Anglia Ruskin University
Note : DRAUP has analyzed top professor profiles based on their number of publications and ongoing research initiatives
56
57
Huntingdonshire Regional College
Peterborough Regional College
City College Peterborough
The College of West Anglia
Community / Further Education Colleges In Cambridge-shire (Doesn’t include Cambridge)
4Number of Community Colleges in
Cambridgeshire (Doesn’t include
Cambridge)
~25-27KNumber Of Student Enrolment
Annually
~500-700Total relevant students pursuing IT/
Computer Engineering talent
Most Common IT Courses Pursued
• Network security
• Computer Networking
• Software Development
• ICT Systems & Principles
• Architecture of computer systems
• Software Engineering
Courses On New Age Technologies
• IT Systems security and Encryption – The College of
West Anglia
• Cyber Security – Peterborough Regional College
• Human-computer Interaction – The college of West
Anglia
“Peterborough,
Huntingdon and
Wisbech are the key
hotspots for Community
Colleges in
Cambridgeshire area
apart from Cambridge.
These colleges offer
certification based
courses on technology
areas such as Cyber
security, IT
Infrastructure,
Networking, Application
Development, etc.”
Cambridgeshire UK: Cambridgeshire has 4 Community colleges with only ~2% of total enrolment in Computer Science related courses
Note : DRAUP’s proprietary talent module was used to analyse courses offered in Cambridgeshire 57
58
58
College Overview
Huntingdonshire Regional
College(College changed to
academy in 2017)
Peterborough Regional
CollegeThe College of West Anglia City College Peterborough
Total Enrolment /
Certifications • Total Certifications - 200 • Total Enrollment – 15,500 • Total Enrollment – 10,000 • Total Enrollment – 400
Relevant Programs and
Courses
• Network security
• ICT Systems & Principles
• Information & Creative
Technology
• Software Development
• Cyber Security
• Architecture of Computer
Systems
• IT Systems security and
Encryption
• Human Computer Interaction
• Computer Networking
• Advanced Computing
• IT and Internet Security
Alumni Profiles
James Pepper
IT Technician, HL Hutchinson
Limited
Current works:
• Working on hardware and software
support systems for Windows
Servers desktops and Android and
administer VPN solution
Jonathan Nicholson
Mechanical Designer, BAE
Systems
Current works:
• Design of routed piping to
maintain heating, ventilation and
air conditioning (HVAC) duct
systems using CAD
Mark Jakes
IT Support, PA Consulting Group
Current works:
• Provide 3rd line support to global
users
• Maintain and upgrade SCCM and
involve in the development and
deployment of windows load sets
Jackie Cairns
Mobilisation Associate Manager,
Accenture
Current works:
• Support on solution realization and
service transition activities and
implement the process of operating
infrastructures
Cambridgeshire UK: Community colleges in Cambridgeshire offer Diploma and Certifications courses mainly focused on IT Infrastructure, Security and Design
Note : The above information is based on data provided by the respective Universities and DRAUP Proprietary Talent Database
59
Professor Profiles – Imperial College and University of Oxford
60
Imperial College London - Professor Profile
Research Works
Student Profile
Post doc PhD Others
Dalal Alrajeh
Assistant Professor
Department of Computer Science
Imperial College London
Email:[email protected]
Phone: : NA
Developing techniques and algorithms that exploit
the semantic properties of system models and
domain knowledge, with humans in the loop, to
yield improved specifications. Application
domains include criticial systems for enhancing
security and tackling (cyber-)crime
Research Highlights
Research Goal:Develop a formal underpinning of the classes of explanation and
repair problems for declarative software specifications (described in
Linear Temporal Logic) that may be resolved through learning and
designing suitable logic-based learning algorithms and tools for
learning correct temporal specifications
Reported Topics: Learning Temporal Specifications of Software
Synthesis for Human-Intensive Systems
Automated Elaboration of Correct Software Requirements
Engineering Forensic-Ready Systems
The Social Ecology of Radicalization: A Foundation for the Design of CVE
Initiatives
Building an Intelligent Crime Linkage System
Development at Lab
Logic-based Learning in Software Engineering,
Technical BriefingBayesian Hierarchical
Ordinal Regression
Computer Graphics Forum
Parameterised Verification for Multi-Agent
Systems
Publications
Ongoing research initiatives: Weakest Environment Assumptions Synthesis
for Generalized Reactivity Specifications
Learning Domain-independent Planning
Heuristics
B.Sc. in Information Technology (First Class), King Saud
University
M.Sc. in Computing (Distinction), Imperial College London
Ph.D. in Distributed Software Engineering, Imperial College
London
Education
Student Research Support
Mohammadreza Biglari
PhD Candidate, Imperial College London
• Davide Cavezza: Weakest Environment Assumptions Synthesis for
Generalized Reactivity Specifications
• Pawel Gomoluch:Learning Domain-independent Planning Heuristics
• Kari Davies: Building an Intelligent Crime Linkage System (ESRC)
Research Supported / Co-Authored:
NA 2 NA
1 315
Journals Book
Chapters
Others
Source : The above information is based on data provided by Imperial College London
Note : DRAUP has analyzed top professor profiles based on their number of publications and ongoing research initiatives
60
61
Imperial College London - Professor Profile
Research Works
Student Profile
Post doc PhD Others
Dr. Krysia BrodaSenior Lecturer
Department of Computer Science
Imperial College London
Email:kb“at”imperial.ac.uk
Phone: : +44 (0)20 7594 8426
Research at the interface of machine learning,
artificial intelligence, and its Big Data
applications- from creative and effective
computing to human-computer interaction, from
machine vision to neurotechnology
Research Highlights
Research Goal:Data-level machine learning to support feature extraction from data
(“Big Data”) to extract readable and insightful relational knowledge
which supports human-understandable machine inference and also
focus on applying a wide variety of feature-based machine learning
techniques in key application areas
Reported Topics: Neural-Symbolic Integration -logic programs can be encoded into artificial
neural networks with an input and output layer, together with a single hidden
layer
Labelled Deductive Systems (LDS) - provide a uniform approach for
investigating different logics
Development at Lab
Logic and Artificial Intelligence - model
checking methodologies for the verification of
autonomous agents has found applications in
autonomous vehicles, service-oriented
computing and security
Spike – development of frameworks,
algorithms, and effective and scalable systems
for engineering structured and probabilistic
knowledge
Publications
29
Ongoing research initiatives: Distributed Abductive Reasoning (DARE)
Inductive Logic Programming (ILP)
Probability in Logic Programming
Application of Logic Programming to Ontologies
Ph.D. in Machine Learning and Time Series Models
Education
Student Research Support
Artur Garcez
Ph.D. in Computing, Imperial College London
• Dr Oliver Ray: Hybrid Abductive and Inductive learning: HAIL
• Calin-Rares Turliuc: probabilistic abductive logic programming, which has
been applied to computational biology
Research Supported / Co-Authored:
NA 5 NA
Source : The above information is based on data provided by Imperial College London
Note : DRAUP has analyzed top professor profiles based on their number of publications and ongoing research initiatives
61
62
University of Oxford - Professor Profile
Research Works
Student Profile
Post doc PhD Others
Marta KwiatkowskaProfessor
Department of Computer Science
University of Oxford
Email:
Phone: : +44 (0)1865 283509
Develop model to describe how systems move
between states by executing actions represented
as state-transition graphs
Research Highlights
Research Goal:Develop modelling and automated verification techniques for stable,
safe, secure, timely, reliable and resource-efficient operation of
computing systems. Design of Model checking verification technique
to deploy certain properties expressed in temporal logic of a system
model
Reported Topics: Noise and Predictability in Molecular Systems
VERIWARE: From Software Verification to ‘Everyware’ Verification
VERIPACE: Design, Analysis and Synthesis Tools for Cardiac Pacemaker
Software
Autonomous Ubiquitous Sensing
Predictable Software Systems
Development at Lab
Complex Systems - Symmetry breaker in
distributed coordination algorithms for
analysing performance and Quality of Service
properties
Prism - Used in distinct fields such as
distributed and cloud computing, wireless
networks, security, robotics, quantum
computing, game theory, biology and
nanotechnology
Publications
Ongoing research initiatives: Mobile Autonomy Programme Grant: Safety,
Trust and Integrity
AFFECTech: Personal Technologies for
Affective Health
B.Sc. in Computer Science in Jagiellonian University
M.Sc. in Computer Science in Jagiellonian University
Ph.D. in University of Leicester
Education
Student Research Support
Luca Laurenti
PhD Candidate, University of Oxford
• Maria Svorenova: Research Officer on Mobile Robotics Programme Grant
and ERC project VERIWARE
• Morteza Lahijianian: Research Officer in association with Mobile Robotics
Programme Grant and ERC project VERIWARE
Research Supported / Co-Authored:
1 10 NA
~300
Source : The above information is based on data provided by Imperial College London
Note : DRAUP has analyzed top professor profiles based on their number of publications and ongoing research initiatives
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University of Oxford - Professor Profile
Research Works
Student Profile
Post doc PhD Others
Alessandro AbateAssociate Professor
Department of Computer Science
University of Oxford
Email:
Phone: : +44 (0) 18656 10767
Formal verification and optimal control of
heterogeneous and complex dynamical models,
built from first principles or learnt from data
Research Highlights
Research Goal:Analysis, verification, and optimal control of heterogeneous and
complex dynamical models -- particularly, stochastic hybrid systems --
and their applications in the life sciences and in cyber-physical
systems (particularly involving energy and power networks)
Reported Topics: OXCAV - Stochastic Hybrid Systems, Probabilistic Model Checking
Synthetic Biology
Autonomous Intelligent Systems
Cyber-Physical Systems (Energy Systems and Networks)
Switched, Hybrid, and Discrete-Event (e.g., MPL) Systems
Software Development at Lab
FAUST2- Tool that generates formal
abstractions of (possibly non-deterministic)
discrete-time Markov processes (dtMP) defined
over uncountable (continuous) state spaces
VeriSiMPL - Toolbox used to generate finite
abstractions of autonomous Max-Plus-Linear
(MPL) systems over R^n
DSSynth - Automated Digital Controller
Synthesis for Physical Plants
Axelerator - Tool for reachability analysis of
Open Guarded Linear Time Invariant systems
through the use of abstract acceleration
Publications
Ongoing research initiatives: Analysis and Design of Hybrid Systems
Hybrid Systems: Computation and Control
Hybrid Systems Biology
Numerical Software Verification
B.S. in Electrical Engineering, University of Padova
M.S. in Computer Science and Electrical Engineering, UC
Berkeley
Ph.D. in Computer Science and Electrical Engineering, UC
Berkeley
Education
Student Research Support
Joe Brown
PhD Candidate, University of Oxford
• D. Adzkiya: Computational Techniques for Reachability Analysis of Max-
Plus-Linear Systems
• M. Prandini: Approximate Model Checking of Stochastic Hybrid Systems
Research Supported / Co-Authored:
NA 11 NA
25 43 623
Theses Book
Chapters
Journals Others
Source : The above information is based on data provided by Imperial College London
Note : DRAUP has analyzed top professor profiles based on their number of publications and ongoing research initiatives
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Demand Supply GAP
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Employed Talent in Digital Job roles is estimated to be ~18K; High demand across these roles can be fulfilled by repurposing adjacent talent pool
1.a
~900* Software Graduates from the 2 Universities
Estimated Job openings across Enterprises and Startups in Cambridge
Total Digital & IT Talent Installed in Enterprises and Startups
~3.4K
~18K
Employed Digital Talent in Cambridge
Employed IT Talent in Cambridge (Talent which can be
upskilled to take up Digital Job roles)
~13.6 K
~4.4 K
Fresh Graduates from Universities
Digital Job Openings
Employed Talent
IT Talent
Digital Talent
Note: : The represented data has been derived using DRAUP Proprietary Talent Module, as of November 201865
Career Progression Case Study: Frequent Progressions from IT Roles to Cloud Computing and Data Scientist job families
• Adobe Dreamweaver CC (2018) - Level 2
• Amazon Web Services: Architecting on AWS
•Probabilistic Machine Learning•Signal and Pattern Processing
• R object-oriented programming and package development
• Bioinformatics: Python for MRI Applications
• Adobe Dreamweaver CS6• Web Authoring: HTML• Drupal: An Introduction
• Cyber Security- Social Media Profiling
• The Cyber Security Programme
• Image Processing and Visualization with LithoGraphX
• Image Processing & Imaging Coding
• Developing computer architectures and computing strategies
• Probabilistic Machine Learning• Signal and Pattern Processing
CLOUD ENGINEER
SOFTWARE DEVELOPMENT
SECURITY
IOT
MACHINE LEARNING
USER EXPERIENCE
COMPUTER VISION
NLP
Certifications offered in Cambridge
Network Engineer Systems analyst DevOps Engineer
IT Consultant Backend Engineer DevOps engineer
Cloud
Engineer
Server Engineer Database Admin
Data Architect
IT Support
Systems Developer
Technical support
Technical Consultant
Database Manager
IT Administrator
Network Analyst
Technical Architect
Network support
Solution Architect
Business Architect
Enterprise Data
Manager
Infrastructure
Manager
ADJACENT IT JOB ROLES
TOTAL HEADCOUNT FOR IT JOB ROLES IN CAMBRIDGE: ~4,400
CAREER PROGRESSION EXAMPLES
Database Admin Backend Engineer Data Engineer
Application
Maintenance
Business
IntelligenceBusiness Analyst
Data Scientist
Note: The represented data has been derived using DRAUP Proprietary Talent Module66
Open Position Analysis - Calibrating degree requirements: DRAUP Open position analysis for Cambridge shows, 29% of the Open positions in IT job roles do not require Bachelors/Masters/PhDs degree
WHO IS REPLACING THEM?
Cambridge Spark provides
continuous professional
development training for
developers and Data Scientists in
24 weeks
Khan Academy is a platform where
experts create content across
various technologies to students to
learn and develop expertise
Coursera is also a content based
platform which offers certification
based technology courses to
students
~29%
10K Jobs
analysed
No-degree
requirement
Backend Engineer
Business Analyst
CAD Design Engineer
Data Scientist/Analyst
Database Administrator
DevOps EngineerFrontend Engineer
Full Stack Engineer/Developer
Hardware Integration Engineer
Java/.NET DeveloperNetwork Engineer
Product Manager
Program Manager
Project Manager
QA/Test Engineer
Retail Sales Consultant
Security Engineer
Systems Analyst
UX Designer
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0 200 400 600 800 1000 1200 1400Open Positions
% J
ob
s w
ith
Non
-Deg
ree
Req
uir
em
en
t
Open Positions Analysis - Cambridge
Note: The represented data has been derived using DRAUP Proprietary Talent Module, updated as on Nov, 2018
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Partnership By Top Peers Across Industries: Companies’ research collaboration is heavily focused on AI/ML
while peer partnerships is across AI, security, computer vision & cloud
AI/MLComputer
Vision/NLPCloud /Security IoT
Research Collaboration Scholarship Start-up Acquisition Corporate Investment Peer-to Peer Partnership
• Apple acquired Cambridge’s voice-recognition spin out VoiceIQ
• Apple is collaborating with the Cambridge to work on ameliorating Siri
• Apple partnered with Microsoft and Amazon on AI research
• ARM funds Simprints that works on application of biometrics
• Geomerics, that specializes in software for calculating radiosity & used in rendering
engines of video games, was acquired by ARM
• Microsoft partnered with Cambridge University for research in Artificial Intelligence.
• It provides support to PhD students & offers post doctoral research position at
Microsoft Research Lab
• Microsoft & Amazon are working on Cortana-Alexa partnership
• Qualcomm acquired CSR, a semiconductor company spun out of Cambridge
• Qualcomm’s Innovation Fellowship supports researchers in AI/ML at Cambridge
university
• Qualcomm is developing voice assistance in wireless headphones in partnership
with Amazon
• Nokia’s Bell Labs is collaborating with Cambridge University to advance AI supported
multisensory personal devices
• Nokia and Cambridge University are co-developing technologies for Morph (flexible
circuitry & nanowire sensing
• AstraZeneca is partnering with Cambridge with focus on visual observations of
molecules behind disease pathways
• AstraZeneca partnered with Microsoft to build business intelligence solutions
• Amazon is collaborating with Cambridge to work on Alexa, core ML and Amazon
devices like Echo smart speakers, Fire tablets, Kindle, etc
• Nokia collaborated with Amazon web services for easier transition to the cloud
Note: The above information is based on data provided by the respective companies and Cambridge University 68
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Hiring Difficulty Analysis
ParametersTalent Supply
from tier-1 universities
Total Employed
talent
Demand (%Open
Positions)Attrition rate Cost of labor
Maturity of Talent Pool
Overall Difficulty
AI/ML Low Medium High Medium High Medium High
NLPMedium Low Medium High High Low High
Computer
Vision Low Low Medium Medium High Low High
Software
EngineerHigh High High Low Medium Medium Low
Security Low Low Low Low Medium High Medium
UI/UX Low Low High Low Low Medium High
IoT Low Medium Low Low Low High Low
Cloud Low Low High High Medium Medium High
Hiring difficulty analysis Insights
• AI/ML, NLP, Computer Vision: Huge competition exists in term hiring top AI/ML, NLP and Computer Vision talent due to low supply, pushing the cost of talent higher compared to other job families.
• Software Developer: Demand for Software engineers is relatively high, due to existence of many enterprise software companies. University Supply of the talent is relatively high due to multiple computer science courses across universities resulting in lower difficulty to hire software talent.
• Security: Due to the low number of Security related courses in the universities, and low demand for security roles as major MNCs/Start-ups focus on Biotech and AI/ML, Hiring difficulty is Medium.
• UI/UX: Low talent availability and high demand is resulting in higher hiring difficulty for UI/UX Job family
• IOT: Cambridge has a few large semiconductor companies employing mature talent with niche IoT skills. Coupled with low demand has resulted in Low hiring difficulty
• Cloud: High difficulty for cloud talent hiring, due to low supply and high demand, as the demand for the role cuts across industries.
Unfavorable Moderate Favorable
Note: DRAUP’s proprietary analysis69
Source : DRAUP
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