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About DraupDraup: An AI Driven Talent Pipeline Mining & Planning Platform

January 2019

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Companies are facing key challenges with respect to workforce transformation

• Over 80% of the Job Roles that will be filled over the next decade do not exist yet• Companies have a very limited understanding of employees’ skills inventory and upskilling potential• Companies fear the entry of technology giants such as Google, Microsoft, Amazon without understanding the specific portfolio

of their work• CIO organizations as a whole, its reporting structure, and the portfolio it owns are key priorities • Technology is highly impacting risk management teams. Physical, Cyber, Customer, Data, Financial and all other forms of risk are

a key priority• Aligning internal roles to the ever changing external job roles is emerging as a key challenge• Companies have a large location footprint due to globalization, but locations aren’t leveraged optimally• Training programs are not tied to career outcomes. As a result, companies report less than 20% uptake of the offered training

programs• University relationships have not often progressed beyond recruiting interns• Mapping personas and building a brand to attract Digital talent pool is becoming extremely critical• The taxonomies of key technology areas such as Cloud, AI, Cybersecurity are rapidly changing (for example, we have mapped 26

job roles on cloud)

By nurturing, developing and attracting the right talent, HR can now impact the top line as AI driven companies disrupt industries

Organizational Priorities and Challenges (across industries)

33

Executive Summary

Global talent pool trending including availability of talent in certain markets to fill mission critical roles

Broad and deep understanding of how peers and near peers are performing and competing for talent

Forward looking view into emerging jobs or capabilities & how they may impact your workforce strategy

Diverse talent pipeline across critical job families and locations

Global location and ecosystem analysis

Ability to source critical talent and develop talent pipeline strategies in key talent markets, globally

Talent Availability Talent Competitors Emerging Talent Talent Pipelines High Cost/ Low Cost Talent Locations

Location Strategy & Planning

Ø In today’s competitive environment, companies are making major shifts in business strategy and are looking for tools to help them visualize the potential impact of shifts over time in the areas of:

Ø Zinnov’s products and service offerings provide a significant advantage to its customers through: • unlimited access to the capabilities built into Draup – an AI- enabled platform mining the most

comprehensive, publicly available data sets across the globe • their team of researchers, consultants and machine learning scientists who will optimize the what and

how of Draup’s capabilities

Source : DRAUP

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4 4

Enabling Business & HR Leaders to Answer Critical Talent Capabilities Questions Using Data and Science

• Understand talent demand and availability of job roles by job families or defined skillsets

• Supply-Demand analysis and gap estimation for any job roles, job families or defined skillsets

• Evaluate and compare current salary costs and growth rates across global locations by job roles

• Identify emerging skillsets where the talent with emerging skillsets are located

• Understand best practices for organization and job role structure for emerging functions

Emerging Talent MarketsWorkforce Supply & Demand Planning• Understand talent patterns and trends based on your

company’s “mission critical” roles• Global location simulation for understanding the direct and

total talent costs across locations for any job roles, job families or defined skillsets

• Talent peers analysis including strategic intent analysis, technology tools adoption, globalization footprint and hiring patterns

• Detailed diversity trend analytics-driven dashboard organized by locations, job families, roles and skills

Talent Patterns and Competitive Trend AnalysesEnabling Informed Workforce Strategy and Executable Talent Planning

Strengthening and Augmenting Recruiting Capabilities

• Discover best fit candidates based on location, job roles, skillsets, defined “hiring fit” criteria

• Identify and pursue the right candidates that are the most likely to join your company

• Identify best fit profiles based on personality essence and psychographic analysis

• Understand detailed characteristics of potential hires including job progression, stack capabilities, tenure type

• Millennial hire mining characteristics, including analysis of signals and attributes from social media

• Learn the best method to engage potential hires based on engagement guidance derived from psychographic and interest analysis

Talent SourcingTalent Identification• Ability to mine and generate “movers and

shakers” analyses based on your company’s unique criteria

• Visibility into metrics to build business cases –including data points like cost components, talent pool availability, and probability to join

Talent Pipeline Trend Analysis

Source : DRAUP

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5 5

Enhancing and Enabling Workforce Strategy & Transformation

Location Intelligence

• Talent Pool─ Employed Talent Pool and

Growth Forecast─ Talent Demand and

Forecast• Cost & Salaries

─ Fully Loaded FTE Cost─ Average Salary for any

Role and Location• Hiring Metrics

• Competitive Intensity• Talent Insights

• Top Skills and Tools Experience

• Major Certifications• Gender and Ethnic

Diversity • Talent Supply

• University Talent Pool• University Curriculum and

Ratings

• Global Workforce ─ Globalization Footprint─ Hiring Patterns – Top Skills,

Roles and Locations─ In Demand Job Roles

Mapping─ In Demand Skills Mapping

• Business and Financial Performance

─ Subsidiaries, Business Units & Products

─ Strategic Intent Analysis─ Financial Performance

• View of Technology Stacks • Outsourcing Partnerships• Partnerships and Investments• Recent Events and News

(Signals)• Executive Job Movements• Partnerships

• Professional Bio─ Detailed Professional

Experience─ Education Background

• Interests─ Personal Interests and

Insights─ Influencers

• Psychographics─ Psychographic Analysis to

Predict the Right Fit─ Communication and

Engagement Guidelines• Professional Social Profiles –

including LinkedIn, GitHub, Stack Overflow, Dribble, Stat Exchange

• Derived Metrics to predict the potential to Hire based on:

─ Relocation Propensity ─ Probability of Promotion─ New/Emerging Capability

• Skills and Tools Experience

View 40+ attributes for any Location and Job role

View 2,500+ attributes that provide multi-dimensional analysis of peer companies

Peer Intelligence Smarter Sourcing & Recruiting

View 50+ attributes detailing hiring resume for 2 Mn+ Profiles

Enabling Talent Management teams with a comprehensive integrated view of the global talent ecosystem

• Location Analysis• Talent Insights for Job roles• Talent Insights and Trends around

Emerging Technologies• Talent and HR trends and Thought

Leadership• Peer group macro organization

structure analysis• Peer Global Micro hub Analysis• Career Progression Analysis• Innovation in community colleges

and smaller universities• Startup Analysis• Executive Leadership Analysis

Consulting On Demand

Strategic Decision Support & Access to Syndicated Reports (BrainDesk)

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6

Draup has the ability to analyze deep characteristics of all emerging technology stacks. Characteristics of Cloud Talent

Note: DRAUP’s Talent Simulation Module: The locations mentioned above are major locations with relevant Cloud Engineer talent and are based on presence of global as well as local Software,

Information Technology, Internet and Research companies. Installed Talents in IBM & Accenture are included under IT Services.

~70% TalentConcentrated in

Top 15 Locations

IT Services Industry Employs

~40% of Talent Pool

Software & Internet Industry Employs

~35% of Talent Pool

Telecommunications,

Banking & Financial

Services Industries each

employs ~5%

Talent Pool

San FranciscoSan Mateo

Sunnyvale

Mountain View

San JoseOrange County

Irvine

San Diego

Tempe

ProvoBoulder

Denver

Colorado Springs

Richardson

HoustonSan AntonioFlorida

Tampa/St. Petersburg

Birmingham

DurhamCharlotte

LouisvilleCincinnati

Columbus

Chicago

Pittsburgh

Nashua

BostonConnecticut

New York City

BrooklynPrincetonNew JerseyPhiladelphia

BaltimoreWashington D.C.

HerndonAlexandria

Talent Hotpots Top Talent Regions

Helena

Boise

Oklahoma City

Lincoln

Fargo

Rapid City

Tucson

Milwaukee

WEST COAST REGIONCloud Focus Companies Major

Employers: Google, Oracle,

VMware, Salesforce.com, Facebook TEXAS AREAEnergy & Utilities, Telecom Hotspot

Major Employers: Exxon Mobil, Halliburton,

AT&T

EAST COAST REGIONSoftware & BFSI Hub

Major Employers: IBM, Verizon,

Citigroup, American Express

SEATTLE AREATech GiantsMicrosoft, Amazon and Expedia

have

12 R&D centres primarily HQs

SOUTHERN COSAT REGIONEmerging Software Hotspot

Major Employers: Amadeus, Ultimate

Software Group, ADP

EAST CENTRAL AREASoftware Hotspot

Major Employers: Fiserv, Allscripts,

BestBuy, Strattec Security

ATLANTA AREATelecom Hotspot

Major Employers: NCR Corporation, COX

Communications

DENVER AREAIT Services Hotspot

Major Employers: CSG

International, TTEC

BOSTON AREAHealthcare Hub

Major Employers: GE Healthcare,

Boston Scientific, Akamai

Technologies

77

Career Progression Analytics: A number of Success Stories of Professionals with non-tech background transitioning into AI/ML through micro certifications

Note : The above information is based on data provided by the DRAUP Proprietary Database.

Hilary DotsonData Scientist at Centre For Human Capital Innovation (CHCI)Education: Ph.D. in Sociology

Nicole H. RomanoCTO & co-founder at StealthEducation: Ph.D. in Materials Science & Engineering

Surya Prakash ManpurData Scientist @ RealPage, Inc.Education: (B.Tech.) Electronics and Communications Engineering

Researcher in Polymer Science

Data Scientist

Data Science Fellow at Insight Data Science + Certification course on statistics and programming

Quality Assurance Analyst - III

Data Scientist

Certificate Program in Big Data Analytics and Optimization (CPEE) Data Science, Big Data Analytics

Teaching Associate in Sociology

Data Scientist

Certificate Program in Exploratory Data Analysis, R-Programming and Data Science.

Postdoctoral Researcher in Radiology

Data Scientist

Data Science Fellow at Insight Data Science

Regulatory Compliance Investigator

Data Scientist

Certificate Program in Python Programming, Data Visualization, SQL and Database

Research Assistant in Biomedical Engineering

Data Scientist

Post Graduate Fellow at Insight Data Science

Sebastien DeryMachine Learning at AppleEducation: Master of Engineering (M.Eng.) in Medical Engineering

David K.Data Science - Analytics at Gemini.comEducation: Bachelor’s Degree in Business Administration

Sarah KefayatiData Science Fellow at Insight Data ScienceEducation: Ph.D. in Medical Physics

88

A platform about potentials and possibilities: Successful Transition to Data Scientist/Big Data Analyst role from various traditional roles

Transformation - Certification/Programmes

• Creative Applications of Deep Learning withTensorFlow.

• Neural Networks for Machine Learning• Deep Learning A-Z: Hands On Artificial Neural

Networks

• PG diploma in Management(PGDM), Analytics and Marketing

• SAS Enterprise Minor Certification

• AWS Certified Developer Certification• Cloudera Certified Associate (CCA) Spark and

Hadoop Developer

Sample Profiles Past Experience

• Business Analyst (2014)• Credit Underwriter

(2013)• Loan Specialist (2011)

• Business Intelligence Analyst (2015)

• Developer VBA (2011)• Account Specialist

(2010)• Offset Printer Operator

(2008)

Workload CharacteristicsRodrigo DomingosBusiness Intelligence Specialist, Travelers

Khushbo Makhija

Data Analyst, Edelweiss Tokyo Life Insurance

Kwasi Opoku

Team Lead Big Data, MetLife

Data Scientist

Data Analyst, Insurance Policies

Team Lead, Big Data Analytics

Creating Machine Learning models to increase the competitive advantage of the company.Working on the Information management, data architecture, dashboard development for Brazilian and United States

Performed exploratory data analysis and identified key factors, reasons and trends in surrender policies in R and Tableau

Skilled in Hadoop ecosystem technology stack , Strategic Planning, in depth knowledge in big data and BI tools

• Claim Business Analyst (2015)

• Claim Processor (2013)• Content Analyst (2013)

New Age Roles

99

Enable untapped talent pool: ~12.8 million working mothers have taken career breaks of 2+ years during their professional life cycle

Women who are economically inactive (0-2 yrs)

Women who started looking for jobs

oighs

Women who returned to work after career break

Women who do not return to labor force

Full Time Part Time

Jobs based on their skill set

Jobs not based on their skill set

Jobs based on their skill set

Jobs not based on their skill set

Women who are content working part time

Women who would like to work full time

63% 37%

30%70%

60% 40%

40% 60%

24% 76%

82% 18%

~12.8 Mn2018 -US – Mothers - Career Break

Note: The represented data is analyzed from DRAUP’s Proprietary Talent Module as well as primary interviews from industry stakeholders

1010

Unique job roles : 7 Primary Skills and job roles were shortlisted and analysed within Big Data & Data Science engineering

Unique Roles Titles Technical or Conceptual Skills Description

Note : DRAUP’s proprietary talent module was used to analyse jobs by job roles and skill type

Analyst - Data Management

Maintains and manages the database; Responsible for performing quality checks on datasets; Ensures correct data schema and syntax; Filters and cleans data"

Database Analyst, Data Management Analyst, Database Developer, Database Administrator

1.

Data Architect

Designs and implementing the technical architecture; Defines and designs data systems, services and technology solutions; Implements and administers data infrastructure

Data Architect, Tech Lead Data Platform, Tech Lead Data Modelling

2.

Big Data / Hadoop Administrator

Supports the Hadoop infrastructure and ensures availability; Responsible for node-cluster configuration, deployment and capacity planning; Monitors and maintains clusters and tunes performance; Responsible for administering YARN and providing support for running and monitoring MapReduce jobs

Creates data pipelines to move and transform data; Responsible for performing transformations to aggregate disparate data volumes into data lakes; Manages data from different sources; Provides support, maintenance, monitoring and troubleshooting for data warehouse processes

Data Warehouse Engineer

Data Engineer, Data Warehouse Engineer, Data Warehousing Specialist, Data Developer, Hadoop Developer, Spark Developer, Hadoop Engineer, Spark Engineer, Scala Engineer, Scala Developer

Hadoop Administrator, Data Administrator, Big Data DevOps Engineer, Hadoop Platform Engineer

3.Database

Engineering

apache, azure, distribute systems, flume, google cloud, gradle, integrations, java j2ee,

database architecture, relational databases, data cleaning, data manipulation, tableau, power bi, excel

hadoop, flume, YARN, mongodb, dynamodb, mapreduce, devops, hbase, hdfs, AWS

hbase, amazon web service, kafka, spark, cassandra, dynamodb, flume, gradle, graph, hadoop, jmeter, json

Skill Definitions

1111

Unique job roles : 7 Primary Skills and job roles were shortlisted and analysed within Big Data & Data

Science engineering

Develops algorithms for conversational

interfaces such as chatbots; Identifies the

modes by which speech can be converted to

data; Develops conversational interfaces

using bot frameworks and platforms

Chatbot Developer, Chatbot Engineer, Conversational UI Specialist, Speech Scientist, Speech Researcher, Speech Algorithm Scientist, Speech Algorithm Engineer, Speech Algorithm Researcher, Machine Learning Engineer - Speech, Machine Learning Scientist - Speech, AI Engineer -Speech, AI Researcher - Speech, Machine Learning Engineer - Conversation, Machine Learning Scientist - Conversation, AI Engineer -Conversation, AI Researcher - Conversation

Applied Data Scientist - Speech

Note : DRAUP’s proprietary talent module was used to analyse jobs by job roles and skill type

Applied Data Scientist - Vision

Computer Vision Scientist, Computer Vision Engineer, Algorithm Engineer - Computer Vision, Computer Vision Engineer, ADAS Engineer, Vision Engineer, Perception Engineer, Deep Learning Scientist, Deep Learning Engineer

Develops algorithms for vision-based

applications such as image or object

recognition applications; Designs vision

algorithms for mapping, localization, scene

analysis, object detection and classification;

Develops a perception based solution

integrating multiple sensing devices within

the size, weight and power (SWaP)

5.Applied AI

Analyses and interprets data (both structured

and unstructured) and generates prescriptive

and predictive insights; Responsible for

generating insights from raw data using

inferential and predictive models; Responsible

for developing new analytical models for the

organization

Data Scientist, Applied Scientist, Data Researcher, Applied Researcher, Data Modeling Scientist, Data Modeling Specialist, Data Modeling Engineer, Data Mining Scientist, Data Mining Specialist, Data Mining Specialist, Algorithm Scientist, Algorithm Engineer, Algorithm Specialist, Machine Learning Scientist, Machine Learning Engineer, Machine Learning Researcher, NLP Scientist, NLP Researcher, NLP Engineer

Data Scientist4.

Unique Roles Titles Technical or Conceptual Skills Description

OpenCV, Tensorflow, Pandas, 3D

Modelling, Adaptive Thresholding,

Caffe, Convolutional Neural Network

Dialogflow, API.ai, Wit.ai, Microsoft

Bot Framework, Bayes Rule,

Bidirectional RNN, Chomsky

Hierarchy

Classification, clustering, decision trees,

dimensionality reduction, logistic

regression, SVM, natural language

process, predictive analytics,

Skill Definitions

1212

Global Micro Hubs: Smaller locations have high talent scalability potential due to increased government spending to strengthen infrastructure, socio-economic condition and start-up & university ecosystem

130+Talent Hotbeds

~20% Of AI Talent is employed across

tier-2 locations in 2018

37Countries will be home to 1Mn Machine learning developers by

2030

Mumbai

Delhi

Atlanta

Los angels

Chennai

Pittsburgh

Pune

Paris

Cambridge

KlonAmsterdam

PhiladelphiaSan Jose

HoustonTampa

Lyon

Orlando

Denver

Shenyang

Singapore

Hyderabad

Detroit

Green BayMinneapolis

Hong Kong

Guangzhou

Nanjing

ShenzhenGainesville

Guatemala

RecifeBogota

Campinas

SantiagoSau Paulo

Lima, peru

Lagos

Durban

Accra

Nairobi

Morocco

Colombo

Jakarta

BrisbaneSydney

Melbourne

AdelaidePerth

SeoulKawasaki

Chongqing

Chengdu

Jilin

Buenos Aires

Ho-chi-minhVizag

Surat

Ahmedabad

Dallas

San Diego

Phoenix

Coimbatore

Stockholm

Chandigarh

BucurestCluj

Gdansk

Kolkata

Cairo

Dubai JaipurSan Francisco

Seattle

New York

Boston

LondonMunich

Tel Aviv

Tokyo

Beijing

ShanghaiBangalore

Hotbeds 2018

Emerging Hotbeds

Source: Zinnov Global Machine Learning talent forecasting modelerZinnov analysis of university programs, fresh ML graduates, digital initiatives of Government and Enterprises, StartupsGeneric Data Science Talent pool not considered as there is noise in the data

Employed AI & Big Data Talent Pool

INDIA

• Smart city project

• Heavy infrastructure spend

• Emerging start-up ecosystem

AFRICA

• Social Start-ups’ Impact

• iHub maturity

• Investment by China

SOUTH AMERICA

• Brazil tier-2 cities

• Corruption impact in Brazil

• Mexico stepping up tech impact

USA/Canada

• Higher real estate cost in East

• Rewiring of Auto Industry

• Less regulation in South

• Move towards south and west

CHINA

• Megalopolis initiative

• One-child policy impact

• Migration towards west

EUROPE

• Brexit’s impact – Shift to Germany

• Job Demands in Eastern Europe

• Companies setting up new EU HQs

1313

Ability to analyze niche talent pools DRAUP analysed Economist talent across 70+ locations to identify top 8 hotspots

Seattle

Los Angeles

Dallas/Fort Worth

Area

Mexico City

Toronto

New York CityWashington D.C.

Buenos Aires

Lisbon BarcelonaRome

MilanZurich Budapest

Paris

Warsaw

IstanbulBucharestZagreb

Munich

Berlin

Copenhagen

Stockholm

OxfordLondon Brussels

Cambridge

Bengaluru

MumbaiPune

Jakarta

Hong Kong

Melbourne

Bengaluru

Taipei

ShanghaiSakyo-ku-Kyoto

Note: DRAUP’s Talent Simulation Module: The locations mentioned above are major locations with relevant Econometric talent and are based on presence of global as well as local Banking, Financial services and insurance companies. The data is not exhaustive

GLOBAL TALENT: ~30,000

70+ Locations Analysed

1414

SecurityArchitect

NetworkSecurityEngineer

Cloud Security DevOps Engineer

BackupAdministrator Risk Analyst Risk

ManagerVulnerability

Analyst DLP EngineerAnalyst End

Point Security

SIEMEngineer

SecurityOperations

Lead

IncidentResponder

Analyst Identity and

Access Management

AccessControl

Administrator

ActiveDirectoryEngineer

IAMEngineer

Washington DC

London

New York

Bengaluru

San Francisco

Over 63% of the current demand is distributed across Active Directory Engineer & Incident Responder roles followed by Security Architect and Risk Manager roles

LEGEND

Cell ColourDemand of the Role at corresponding

location

Note: The above represented heat map is created on basis of number of jobs posted at each location in job portals like Public LinkedIn, Indeed, Monster, Naukri, and others updated in Sep, 2018

Very Low

Low Med High Very High

Major Cybersecurity Job Roles In Demand Across Top 5 MSAs

1515

~21% of the global cybersecurity talent pool is distributed across top 10 MSAs globally. Washington D.C. employs the highest cybersecurity professionals globally

Top Locations across the globe with major Cybersecurity talent Composition

~500KDirectly employed in technology companies*

~19%of the global cybersecurity talent is employed

across top 11 cities in the US

~60%Cybersecurity talent have 10+ years of

experience

12,0007,000

9,000

24,000

10,000

13,000

8,000

14,000

23,000 10,000

20,000

Bay Area New York

London, UK

Bengaluru

MumbaiWashington D.C.

Toronto, Canada

UAE

Singapore

Sydney

Paris

ItalyMalaysia

Johannesburg

Chicago Area

Los Angeles

Denver Area

Detroit

Houston, Texas

Minneapolis

New Delhi47,000

15,000

Note: The represented data has been collected from multiple job portals, news articles and Draup Proprietary Database updated as of Sep, 2018; Technology companies include Product Companies, Service Providers & Startups; Map Represents only locations with majority Talent Distribution

9,000

8,500

8,000

33,00014,000

9,00012,000

Note: The represented data has been collected from multiple job portals and Draup Proprietary Database updated as of Sep, 2018.

6,100

Amsterdam

Frankfurt

8,100

Sweden

17,000

Dallas/Fort

8,000

Israel

7,000Ireland

7,000

São Paulo

5.600

Madrid Area

12,500 Philippines

3,200

Reading, UK

2,100 Prague

1,900 Helsinki2,000

Cologne

9,000

1,300 Moscow

2,500

7,000Chennai

Greater Boston Area

15,000

8,500Pune

9,000

Poland

Top 10 MSAs

1616

Language Analysis: All enterprises will be ramping up Spanish language services across products and services

Greater Seattle Area

YakimaPortlandSalem

SacramentoStockton

El Centro

Santa RosaVallejo

FresnoSanta Cruz

Merced

Porterville

Modesto

Los Angeles

San FranciscoSan Jose

Salinas BakersfieldSanta Barbara

OxnardOntario

San DiegoTucson El Paso

EdinburgBrownsville

Corpus Christi

San AntoniaHouston

Greater New Orleans Area

MiamiNaples

Fort Myers

North PortSt. Petersburg

Lakeland

Greater Atlanta Area

CharlotteOklahoma City

Greater Chicago Area Greater Detroit Area

ClevelandGreater Boston AreaWorchester

ProvidenceHartford

New HavenBridgeport

AllentownGreater Philadelphia AreaBaltimore

Note: DRAUP’s Talent Simulation Module: The locations mentioned above are major locations with relevant Econometric talent and are based on presence of global as well as local Banking, Financial services and insurance companies. The data is not exhaustive

60-90%

30-60%

0.5-20%

17

Learning Propensity Analysis: Data Science Deep Dive: CMU, MIT and Stanford offer most advanced courses but smaller universities are catching up

Top AI/ Big Data Talent

Universities

Beginner Courses

Intermediate Courses

AdvancedCourses

CARNEGIE MELLON

UNIVERSITY

Cognitive Science

Logic Programming

Machine Learning

Computational Biology

Computer Vision

Neural networksAI &

Manufacturing

UNIVERSITY OF CALIFORNIA

BERKLEY

Decision TheoryProbabilistic-Reasoning

Genetic AlgorithmRobotics

Problem Solving

Neural NetworksIntegrated AI-Architecture

MASSACHUSETTS INSTITUTE

OF TECHNOLOGY

Cognitive Science

Decision Theory

Computational-Biology

Philosophy of AI

Neural Networks

STANFORD UNIVERSITY

NLPMachine Learning

Cognitive ModellingRobotics

Distributed AI

Small Universities/Community Collages Beginner Courses Intermediate Courses Advanced

Courses

THE UNIVERSITY OF TEXAS Speech and Machine Learning

NLPAutomata Theory

Combinatorics and Artificial Intelligence

SONOMA STATE UNIVERSITY Adversarial Game-Tree

SearchFuzzy Logic

Bioinformatics Neural NetworksGenetic Algorithms

POMONA COLLEGE Human Computer InteractionMachine Learning

NLPRobotics

Computer Vision

Biological Problems through Computational Methods

AZUSA PACIFIC UNIVERSITYApplied Machine Learning

Big Data Analytics & Technologies

NLPResearch Psychology

Predicting Chronic Bronchitis Symptoms Using Machine

Learning

WORCESTER STATE UNIVERSITY

Database Design and ApplicationsData Mining

Big Data Analytics Capstone Artificial IntelligenceRobotics

WICHITA STATE UNIVERSITY Artificial IntelligenceRobotics

YOUNGSTOWN STATE UNIVERSITY

Applied Artificial IntelligenceData Warehousing and Data

Mining

Artificial Intelligence in Game Design

Artificial IntelligenceCloud Computing and Big

Data

UNIVERSITY OF SOUTHERN MAINE Machine Learning

Artificial Intelligence and Data Mining

Autonomous Robots

Courses offered by top universities Courses offered by niche universities and Community colleges

Course maturity Low Medium High

Note : DRAUP’s proprietary talent module was used to analyse millennial moments across various cities

1818

Draup is one of the first platforms to arrive at maturity index for talent: Optimal prospects for ML/Data science talent were identified based on suitability of talent availability and maturity index

Google

TelstraAtlassian

Amazon Web Services

IBM Commonwealth BankSuncorp Group

ANZ Bank

Ambiata

QuantiumExpedia

Woolworths Group

IAG ROKT

Westpac Group

Maturity (Years of experience, Cost, projects and skills)

Tota

l Ins

talle

d M

L/D

ata

Scie

nce

Tale

nt

• Commonwealth Bank, Australia is developing ML technology to help with cyber security, fraud detection, regulatory compliance and power big data to use in reducing risk

• Commonwealth Bank has unveiled a chatbot “Ceba” that uses AI to assist customers with tasks such as card activation, checking the account balance, making payments, or getting cardless cash etc.

• Westpac Group is working with start-up Red Marker to use natural language processing techniques to detect content at risk of breaching legal regulations as the content is being created.

• ANZ Bank has been working on Robotic Process Automation to streamline back office work, including helpdesk support and payroll administration.

ML Talent (Availability vs Maturity Index)

Note- Top 15 companies are analysed based of years of experience, cost, key projects and skill types

Entry-Level Talent Pool Mature Talent Pool

Niche Talent PoolLimited Talent Pool

1919

Tracking Micro Certifications: Cisco, Microsoft and VMware are the top companies having partnership with universities in Ireland for certification courses in Networking, Security and Server Management

Major Certifications

University Name Certification CourseCork Institute of Technology CISCO Certified Network AssociateCork Institute of Technology CISCO IT Essentials 1/CompTIA A+Cork Institute of Technology VMware vSphere Fast Track ICM & Optimise and Scale 6.0

Athlone Institute of Technology Microsoft Office Specialist (MOS)Athlone Institute of Technology Cisco Introduction to Networks (CCENT)

Griffith College Ireland Cisco Certified Network Associate (CCNA)College of Computer Training Cisco Certified Network Associate (CCNA)College of Computer Training Microsoft Certified Solutions Associate (MCSA)

Dorset College CompTIA/CISCO A+ Certificate in IT EssentialsDorset College Cisco Certified Network Associate (CCNA)Dorset College Cisco Certified Network Professional (CCNP)Dorset College MCSA - Microsoft Windows Server

Institute of Technology Blanchardstown Certificate in CISCO-CCNAInstitute of Technology Blanchardstown Certificate in CISCO - Network Security 2Institute of Technology Blanchardstown Certificate in PC Maintenance and Networking with CCNA

Institute of Technology Tallaght Cisco Certified Associate Programme

Note : The above information is based on data provided by the respective universities and DRAUP Talent Database

2020

Matching Startups with funding and headcount: Singapore’s ML start-up ecosystem is optimal for acquisition and acquihire, for niche ML/data science skills

Note : DRAUP’s Talent Simulation Module analysed ~1000 start-ups in Singapore to identify top ML based start-ups

Startup Name Key Offerings Company Headcount Total Funding Marquee Customers

Visenze Artificial Intelligence in visualsearch and image recognition 50-100 $ 14 Mn Rakuten, ASOS, Zalora, Carat Lane

CashShield Fraud Detection, Security, Payments

10-50 $ 5.5 Mn Razer, T Mobile, Voyagin, Vodafone

Taiger Automatic Document InformationExtraction through AI 10-50 $ 5.8 Mn Bank of America , Housing and Development Board,

Vodafone, Manulife, NSCS

JobTechArtificial Intelligence and Big Data Analytics start-up that provides

optimized job matching tools10-50 -

Active.ai Enterprise ML Platform for Financial Services

50-100 $ 3.5 Mn

NugitAutomated data analytics,

visualizations, storytelling and sharing

10-50 $ 5.2 Mn Facebook, IBM, Samsung, Audi, NewsCorp, Sanofi

Vi Dimensions Big Data and Machine Learning in surveillance 10-50 $ 1.5 Mn

Tookitaki Machine Learning, Finance, FinTech 10-50 $ 1.23 Mn

BluePool Machine Intelligence, Capital Markets

10-50 -

Jumper.AISocial Media Marketing, Artificial

Intelligence, Information Technology

10-50 - Target, Disney, Volvo, Unilever

2121

Cluster Analysis of job corpus: DRAUP’s analysis of Job postings by 1000 companies estimates the total AI demand to be ~100K

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New

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D.C.

Chic

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Los

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Atla

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Dalla

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Phila

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Denv

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San

Dieg

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Aust

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Min

neap

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Data Modelling/Analysis

Machine Learning

Computer Vision

NLP Engineer

Image Recognition

Speech Recognition

Data Scientist

Job Roles Top

MSA

s

<40% 40% to 25% 25% to 15% 15% to 5% <5%

Tota

l Job

Po

stin

gs

40K

20K

7K

5K

3K

4K

9K

LEGEND Cell ColourConcentration of Role in the Location

2222

Optimality of Job Descriptions: Let us look at the recent AI Job description posted by a large Industrial company (Slide1 of 2)

• PhD in Computer Science, Electrical Engineering or related field, with 10+ years of experience in developing, implementing and managing AI/ML related projects

• Strong technical skills on machine learning/AI with proven track record. These technical skills include, but not limited to, regression techniques, neural networks, decision trees, clustering, pattern recognition, probability theory, stochastic systems, Bayesian inference, statistical techniques, deep learning, supervised learning, unsupervised learning

• Experience on developing a long-term analytics innovation strategy and driving those across various levels in the organization

• Strong technical knowledge on big data technologies, cloud, and opensource software tools• Outstanding track record of successful solutions design in the digital space• Demonstrated strong project leadership skills and relevant experience on IoT, connected systems, and

applications• Prior experience in leading innovation teams• A strong track record of starting new activity areas and E2E management of projects related analytics

enabled solutions and transferring to production as appropriate• Development of R&D strategies for new business offerings including market assessment and business case

formulation• Working with industrial partners and /or affiliations, track record in establishing partnerships• Experience in multicultural/global team and influencing decisions at the highest management levels.

2323

Optimality of Job Descriptions: Translating this job description into skills results in over 55 skills (Slide2 of 2)

12+ LEADERSHIP/BEHAVIOURAL SKILLSSTRATEGIC PLANNING

TECHNOLOGY LEADERSHIP

BUSINESS ANALYTICS

PROACTIVE

OPERATIONS PLANNING

BUILD PARTNERSHIPS

ANALYTICAL DECISION MAKING

PROCESS IMPROVEMENT

CRUNCH TIME EXECUTION

EXCELLENT COMMUNICATION

COLLABORATIVE

DECISION MAKING

33+ TECHNICAL SKILLS REQUIREDAPACHE OPEN NLP PROJECT PLANNING NLTK

PREDICTIVE MODELING NEURAL NETWORKS WEKA

STATISTICAL MODELS STOCHASTIC MODELING IMAGE PROCESSING

SAS/SQL DATA WRANGLING OPEN CV

CAFFE TORCH APACHE HIVE

HADOOP SAAS SAS/STAT

SOLUTION DESIGN ANALYTICS TENSORFLOW

APACHE MXNET THEANO KERAS

CNTK SCIKIT-LEARN H2O

SPARK MLLIB APACHE MAHOUT

JAVA SCALA SAS

55+ Technical/Behavioural Skills required for the Entry level Data Scientist Job Role posted by a Large Industrial company

24

24

Location sustainability analysis: Major global hotspots for Software talent will be facing for sustainable water infrastructure challenge

MumbaiPune Chennai

Kuala Lumpur

Taipei CityShanghai

Seoul

Shenyang

ndSydney

Wuhan

Porto

BarcelonaMadridValencia

Sao Carlos

Brisbane

Perth

Bogota

Rio de JaneiroSao Paulo

Santiago

San Jose

Mexico City San AntonioHouston

Miami

Tampa JacksonvilleMemphis

AtlantaRaleigh

Spring Field

TulsaWashington DC

Baltimore

Cairo

Nairobi

Cape Town

Abu Dhabi

Harare

Qatar

Durban

LagosAccra

Lima

Colombo

Belfast

London

EdinburghStockholm

Copenhagen

WarsawBerlin

Gent

Rome

Basel

HangzhouSan Francisco

Seattle

Vancouver

New York

Quebec

Montreal

Analysis of top 150+ global software talent hotspots

Regions in white have abundant supply of water or very low data is available

SOURCES: DRAUP Talent Platform was leveraged to analyse global software talent hotspots | World Resources Institute (WRI) was leveraged to analyse global water consumption

Potential Risk~50% of the water supply is withdrawn , the

cities have good infrastructure

Scarcity Scarcity of water due to low infrastructure

investment or High consumption

Long term risk~25% of the water supply is withdrawn for

Industrial, agricultural and domestic needs.

Approaching danger~75% of the water supply is withdrawn . Low

economic development

2525

Calibrating degree needs: DRAUP Open position analysis for Dallas shows, 27% of the Open positions in IT job roles do not require Bachelors/Masters degree

WHO IS REPLACING THEM?

Lambda School trains students in technology skills and provides them job guarantees, they recently raised

$14M to scale operations across USA

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

Holberton school emphasises on project based learning and helps

students improve their technical skills

Note : DRAUP’s proprietary talent module was used to analyse open positions data for Dallas

~27%

9K Jobs analysed

No-degree requirement

Android Developer

Applications Developer/EngineerCloud Architect

Cloud Engineer

Data Architect

Data Center Technician

Desktop Support

Frontend Engineer

Full Stack Engineer

iOS Developer

IT Consultant/Business

Consultant

Technical Support Engineer

Java Developer

Network Administrator

Performance Engineer

QA Test Automation Engineer

Salesforce Developer

Security EngineerSolutions Architect

UX Designer

0.20

0.22

0.24

0.26

0.28

0.30

0.32

0.34

0.36

0.38

0.40

0 50 100 150 200 250 300 350Open Positions

% J

obs

with

Non

-Deg

ree

Req

uire

men

t

Open Positions Analysis - DALLAS

26

26

Niche Professors Analysis: Professors at niche/small universities are driving high level research in AI/ML areas and skilling university students. HR has no easy access to this data.

2

Journal ArticlesTheses

2

Book Chapters

Research Works

Student Profile

Post doc PhD Others

2 8 25

Joe SongProfessor

Department of Computer Science

New Mexico State University

Email: [email protected]

Phone: : +1-575-646-4299

Dr. Song's research lab develops efficient

computational and statistical methods to model

mechanisms of complex biological systems

Research Highlights

Research Goal:

Three-association framework (3AF): (A statistically effective and

computationally efficient algorithmic framework to detect, represent,

and manipulate functional, temporal, and statistical associations

among random variables, to account for causal interactions in

dynamic biological networks)

Reported Topics: q Multivariate likelihood joint quantization;

q Generalized logical networks

q Data-driven discrete and continuous dynamical system modeling

q Data stream clustering

q Computational systems biology applications (Biomass conversion in yeast,

Cell cycle exit modeling in fruit fly, Gene interactions in cerebellar

development)

Software Developed at Lab

Ø ChiNet - Pathway and sub-network rewiring

based on non-parametric discrete models

Ø Ckmeans.1d.dp - A fast dynamic

programming algorithm for optimal univariate

clustering

Ø CPX2 - Comparative chi-square analysis of

interactions. It is implemented as a program for

comparative chi-square analysis of interactions

across different molecular contexts

Ø FunChisq - The functional chi-square test

used by NMSUSongLab and claimed a Best

Performer in DREAM8 Network Inference

Challenges

ØQ-method - Pathway and sub-network

rewiring based on parametric linear and

nonlinear differential equation models

Publications

29 40

Others

Ongoing research initiatives: q Statistical computing

q Computational systems biology

q Neuronal signal analysis

q Computer vision

Specialtiesq Computational Systems Biology; Statistical

Computing; Computer Vision

B.S. in Electrical Engineering at Beijing University of Post and

Telecommunications

M.S. in Electrical Engineering at Beijing University of Post and

Telecommunications

Ph.D. in Electrical Engineering at University of Washington

Education

Student Research Support

Sajal KumarPhD Candidate, New Mexico State University

• FunChisq: Chi-Square and Exact Tests for Model-Free Functional

Dependency

• Simulating noisy, nonparametric, and multivariate discrete patterns

Research Supported / Co-Authored:

27

27

Niche Professors Analysis: Partnering directly with AI professors in smaller universities: AI Professor profiles at East Coast Small Universities/Community Colleges

Professor University Research Focus Areas Citations Contact AffiliationsPatrick McDonaldProfessor of Mathematics

New College of Florida Probability and Stochastic Analysis; PDE; Optimization; Data Visualization;

Artificial Intelligence1622

[email protected]

941-487-4375

Carnegie Learning; European Southern Observatory; Sloan

Fellows

Ankur AgrawalAssociate Professor

Manhattan College Medical Informatics; Data Mining139

[email protected]

718-862-7733

New Jersey Institute of Technology; Manhattan College;

Stanford University; University of California

Amalia RusuAssociate Professor

Fairfield University Human Interactive Proofs; Document Image Analysis;Pattern Recognition; Image Processing; AI; HCI; Web Security

NA [email protected]

Sunil ShendeAssociate Professor

Rutgers University-Camden

Algorithmic Game Theory; NLP; Parallel and Distributed Computing; Data Compression; Mobile Computing; Data Compression and Encoding; Big Data

[email protected](856) 225-6122

Hewlett-Packard

Roy GeorgeAssociate Professor

Clark Atlanta University Data Mining; Knowledge Management; Information Assurance

and Soft Computing; Artificial intelligence; Machine learning; NA

[email protected]

ASELSAN

Brian RussellAssistant Professor


OS, Artificial Intelligence, Networking, Artificial Languages, Software Engineering, and Psychology of Software Development


NA

Hsin-Chu Chen,Associate Professor

Clark Atlanta University High Performance/Applied Parallel and Scientific Computing; Computer Networking; Algorithm Analysis and Design

[email protected]

NA

Suneeta RamaswamiProfessor


Computational Geometry and Applications; Mesh Generation; Computational Statistics; Algorithms; Mesh Generation; Robotics; Computer Graphics.


AT&T Labs

Scott FreesProfessor

Ramapo College of New Jersey

Web Development; Human-computer interaction; Virtual reality; Software engineering; Database Systems; Computer Graphics


Sarnoff Corporation

Sourav DuttaAssistant Professor


Data Structures and Algorithms; Big Data; High-performance Computing; Hardware/software co-design; Machine Learning


Southern Illinois University Carbondale

Amruth KumarProfessor


Organization of Programming Languages; Artificial Intelligence; Computer

Graphics; Intelligent Tutoring Systems1494


NA

John Doucette,AssistantProfessor of CSc

New College of Florida Artificial Intelligence; Multiagent Systems; Machine Learning; Social Choice Theory


University of Waterloo

David GillmanAssistant Professor

New College of FloridaImage Processing; Health Informatics; Data Science; Artificial Intelligence


VMware;Akamai Technologies

mailto:[email protected]












2828

Career Progression Analytics: DRAUP analysed 24K AI/ML professionals, ~15% of the employed talent has leveraged micro certification courses to enter into AI/ML space

DataScientist

Category Past Role Current Role

Note : The above information is based on data provided by the DRAUP Proprietary Database.

Engineering3-4 years

1-2 years2-3 years

Average Time for transition

Research

Government Administration

Judiciary

1-2 years

2-3 years

1-2 years1-2 years

3-4 years3-4 years3-4 years



StatisticianOperations EngineerResearch Assistant

Econometrician

Social Scientist

Research Assistant: Cultural Social Science

Law and Human Rights Researcher

Attorney at lawBusiness Law Attorney

Legal Economist

Behavioural Science ResearcherSocial Science Researcher

Statistics Teaching Assistant

Business/Data Analyst 1

Software Development ExecutiveQA Analyst/Software Testing Engineer

IT AdminData Warehouse Engineer/Database administrator

Algorithms Developer/EngineerWeb/Java Developer

2-3 years6-7 years

1-2 years2-3 years

~85% profiles

~15% profiles

2929

Microhubs is the next generation location strategy where emerging locations with small teams can be leveraged

DRAUP’s AI/ML Talent projections for 2025 estimates emergence of 80 Hotspots, supply is expected to grow to be around 220K

Source: Zinnov Global Machine Learning talent forecasting modelerZinnov analysis of university programs, fresh ML graduates, digital initiatives of Government and Enterprises, StartupsGeneric Data Science Talent pool not considered as there is noise in the data

Pullman

Portland

Eugene Boise

San JoseOrange County

San DiegoLas Cruces

Boulder

Colorado Springs

Kansas City

Lawrence

Lincoln

Ames, Iowa

Iowa city

St. LouisRolla

Fayetteville

Mansfield

San AntonioHouston

New Orleans

Atlanta Columbia

ClemsonKnoxville

CharlotteRaleigh Durham

NorfolkRichmond

FairfaxBaltimore Newark, Delaware

LowellHartfordNewark

Williamsburg

Burlington

Cincinnati

Indianapolis

Tampa St. Petersburg

Santa clara

Hotbeds 2018 Hotbeds 2025

Greenville

Phoenix

Skilled Talent: AI/ML talent installed across US is estimated to be around 220K, 60% of which is installed in top 8 major cities. The Demand for AI/ML talent will reach 2.1M by 2025

University Supply: DRAUP’s Talent module analysed university curriculums introduced in AI/ML areas of over 2K large and small universities and Community colleges across the Tier1/2/3 cities and estimates the rise in supply for AI/ML talent across these 80 cities

Talent Migration Drivers: Higher real estate cost in top east coast locations, Less regulation in the Southern states, Rewiring of the Automotive Industry are major reasons driving the movement of Talent across southern and western cities

San FranciscoDenver

Chicago

Washington DC

New York

Boston

Seattle

Dallas

3030

Talent Cohorts Analysis: An overall talent pool study must consider peer level study to calibrate similar talent

LEGEND Cell ColourConcentration of Role in the Industry

Top EmployersMachine Learning Computer VisionNatural Language

ProcessingData Scientist Data Architect

Computer Software

Information Technology (Services)

Internet

Healthcare/Biotechnology

BFSI

Automotive

Telecommunication

Electronics/Semiconductor

Others

<5% 5% to 15% 15% to 25% 25% to 40% >40%

Note : DRAUP’s proprietary talent module was used to analyse technology talent across different Industries. The Talent count is inclusive on Applied Scientist counts in each of the domains.

3,000 3,500 7,000 5,000 3,500

Microsoft, SAP, IBM, Adobe, Oracle, Intuit,Vmware

Accenture, Wipro, TCS, Cognizant, Infosys, KPIT

Flipkart, Amazon, LinkedIn, OLA, Uber, Google

IQVIA, UHG, GE Healthcare, Philips Healthcare

Goldman Sachs, JP Morgan Chase, Wells Fargo, HSBC, Citi, Morgan Stanley

Mercedes Benz R&D, Bosch, Continental, Ford, Volvo

Ericsson, Nokia, Cisco, British Telecom, Mahindra Comviva, Vodafone

Intel, Texas Instruments, Qualcomm, Applied Materials, NVIDIA, Honeywell, AMD

Fractal Analytics, Mu Sigma, Impact Analytics, GE Global Research, Shell, GE Aviation, Boeing

22KAI/ML Professionals in Bangalore

3131

Recruiter Productivity is impacted due to relying only on key word search in existing profile platforms . DRAUP provides a single platform to analyze data for talent within and outside of organization

How DRAUP can help

• Draup provides an overview of Talent Supply& Demand snapshot and personalized hiringdifficulty/costs of hiring for a given role in agiven location

• Analyze the demographics, diversity, and education background of potential recruits across job roles and locations

• Draup can provide a 360o view of the skillsets required for each Job Role

• Draup generates Skills Maps that help workforce planners to understand the key strengths and capabilities of both external and internal workforce

• Draup provides qualitative indicators and a psychographic profile of each candidate that fits the search criteria

• Example:- Employee Type Tags, Personality Inference, Working Style and Influencers

Updating distributed tracking Systems and

maintaining an employer brand

Crawling Resumes and identifying

relevant/missing Skillsets

Interacting with potential candidates,

conducing pre-hire assessments etc

Recruiting Tools Recruiter Tasks

Skills Mapping

Learning Management Systems

Pre-Hire Assessment

Applicant Tracking Systems

Recruitment CRM

• Bullhorn• Taleo

• Smashfly• Yello

• Hirvue

• Litmos LMS• Talent LMS

• Hirvue• Hundred5• HackerRank• Pymetrics• Quodeit

Workforce Planning

Full-stack View

Soft-skills and Cultural Fit

3232

Catchment Area Analytics: US- Core 5G talent landscape: ~12,000 5G engineers; ~60% of the talent is concentrated at Six 5G catchment areas

ATLANTA

OKLAHOMA CITY

WACO

NEW ORLEANS

HOUSTON

RALEIGH

CHARLOTTE

SAN FRANCISCO

LOS ANGELES, & SAN DIEGO

NEW YORK

BOSTON

WASHINGTON DC

CHICAGO

LAS VEGAS

PHOENIX

KANSAS CITY

SEATTLE

Ann Arbor

Denver

Cornell UniversityYale University

University of California, LA

North-western University

NYU

University of Texas

Purdue University

Boston University

Pennsylvania state university

University of Colorado Boulder

Vanderbilt University

Georgetown University

University of FloridaTexas A&M University

North Carolina State University

Syracuse University

University of Nebraska-Lincoln

MATURE 5G TALENT HOTSPOTS

5G Major Telecom Network Provider presence )

Top universities for 5G talent supply

AT&T

T-Mobile

University of Texas At Dallas

Dish Network

Cox Communications

US Cellular

AT&TAT&T

AT&T

AT&T

Verizon

Verizon

Verizon

Verizon

Catchment Areas

Century Link

Comcast Corporation T - MobileSprint

Sprint Corporation

Potential 5G Markets

CenturyLink

University of South Florida

DALLAS

Charter Communications

~12,000Total 5G R&D Talent across the

country

2k+ Talent33%

22%

20%

17%

9%

Washington DC

Bay Area

New York Area

Dallas Area

San Diego Area

1.4k+ Talent

1.3k+ Talent

1.1k+ Talent

600+ Talent

Note : DRAUP’s proprietary talent module was used to analyse jobs by job roles and skill type across locations

Verizon

AT&T

Core 5G

33

Sociology Researcher Data AnalystDecision Sciences

Researcher

Social ScientistQuantitative Social

Science Researcher

Data and Research Analyst

II

Behavioural Coach Research Assistant Applied Social Scientist

Operational Culture

Social Scientist Research Specialist Data Mining Analyst

Social & Behavioral Sciences

Research Assistant

Graduate Teaching

Assistant - Sociology

Graduate Teaching Associate -

Social Science Statistics

Law and Human Rights

ResearcherSocial Researcher Senior Data Analyst

Data Scientist Job Progression Roadmap

Current RoleInitial Role

DataScientist

Past Role Previous Role

Learning Propensity Analysis: Diverse set of professionals have entered and exceled in the areas of AI/Data Science: Mapping Role Progression Possibilities is a key component of talent pipeline building

~2,000 Data Scientist profile analysed Hilary Dotson

DESIGNATION: Data Scientist at Center For Human Capital Innovation (CHCI)

Education: Ph.D. in Sociology

Specialization: Medical Sociology, Racial Inequality

Research Areas: Machine learning, Statistical

Modelling and Qualitative & Quantitative research.

Role Transition: Started as Research Assistant of

Social and Behavioral Sciences at University of

Central Florida Dotson then moved on to teach

Sociology and also did courses in Exploratory Data

Analysis, R-Programming and Data Science.

Thomas HilbigDESIGNATION: Data Science Researcher at Texifter LLC

Education: Ph.D. in Data Science & Criminology

Specialization: Machine Learning, Crime Statistics

Research Areas: Data Science, Machine Learning,

Crime Statistics and Statistical Computing.

Role Transition: With Degrees in Criminology and

Social Research Thomas explored Data Mining

Techniques for Social Sciences and identified Real-

Time reports using Machine Learning.

Sample Profiles

Note : DRAUP’s proprietary talent module was used to analyse job progressions across data science roles

3434

Organization Structure Analytics: Liberty Mutual : Presence of both horizontally aligned (by HR divisions) & Vertically aligned (Business Line wise) HR teams

Recruitment &

Leadership Hiring

Note : This is an indicative structure based on relevant Job Descriptions & Workloads as mentioned in Job Postings and Draup’s Proprietary Talent Database.

Organization Structure for US Operations

Stephanie TurnerDirector, Diversity &

Inclusion Strategic Programs

Boston (1.5yrs)

Thomas OksanenVP, Employee Benefit

Boston (4.5yrs)

Benefits ManagerBoston

Brenda RuizAsst. Director, Diversity &

InclusionBoston (1yr)

Alejandra VidaurretaDiversity Executive Talent

RecruiterFlorida (0.5yr)

Benefits Diversity and Inclusion

Cara HadleyVP, Sr. Talent Advisor

Boston (11+yrs)

Dennis Goebel VP, Enterprise Talent

Acquisition ProgramsFlorida (9+yrs)

Jodi WallachVP, Talent Acquisition

Connecticut (8+yrs)

Maura QuinnAVP, Campus

Recruiting ProgramsBoston (12+yrs)

Alyson YablonskieSheppeck

Director Talent Acquisiti

onBoston (3+yrs)

April Grogan Director, Recruitment -

Talent AcquisitionKansas (11+yrs)

Lisa Grasso Director Talent Acquisiti

onBoston (0.5yr)

Brian MoorhouseHead , Executive Recruiting

and Enterprise SourcingPhiladelphia (1yr)

Rebecca Virtanen PehSr. Recruiter, Technology

& Emerging SkillsBoston (1.5yrs)

Devony ColeyExecutive Recruiter

Boston (2.5yrs)

Nick PlanteSenior Recruiter

Boston (2.5yrs)

Campus Recruitment Manager

Elizabeth RaymondRecruiting Coordinator

Boston (0.5yr)

Sarah CoderreSr. Benefits Specialist

Boston (3.5yrs)

Tiffany TaylorCampus Recruiter

Houston, TA (6.5yrs)

Years in current organization is mentioned

3535

Amazon

Target

Walmart

Kroger

Hospital Corporation of America

Cardinal Health

National Healthcare Corporation

TrustPoint Hospital

Farmers Insurance Group

Advance Financial

First Tennessee Bank T-Mobile

Ascend Federal Credit UnionConvergyx

Talent Poaching Analytics: Amazon, HCA, Target, Cardinal Health and Walmart are optimal peer employers for desired talent pool in Murfreesboro

Tale

nt S

uppl

y Sc

ale

Recommended Peer Employers

Low Target Peer Employers

Talent Supply vs Talent Affinity

Talent affinity (Attrition rates, Cost effectiveness, Industry relevancy, Training Effort, Growth factors)

• Amazon and Walmart have well trained and large number of telesales talent pool which can be tapped for hiring . Flexible timings, work life balance and regular work shifts are key retention factors for these companies.

• Healthcare providers such as HCA and Cardinal Health have large scale training programmes and huge pay benefits. In spite of these benefits, attrition is expected to be relatively high considering limited growth opportunities.

Customer Support Representative

3636

Platform Component: Ecosystem Insights

Comprehensive data-driven analysis of Peer’s Globalization and talent strategies

Global Work Characteristics- R&D and IT Center Presence- Global Workforce Distribution- Key Programs in all Locations- Leaders Across all Locations (exec movement)

Digital Tech Stack (What tools are they using?- Insight into all the Digital Platforms - Tools and Technologies used by a company

Hiring and Job Opening Analysis- Hiring Trends across location and Sub Verticals- Sub vertical is a deeper element that tracks by

granular subject area

- Insights into key executives which can be hired

Key Skills Hired in last 6 months

Current and Past Job Openings

3737

Platform Component: Ecosystem Insights

Outsourcing Insights- Insight into all the outsourcing partners- Insights around verticals/sub-vertical and

locations leveraged by partners

Executive Movement- list of senior stakeholders who have joined,

exited or been promoted over the past 12 months

Account Compass- insight into the various metrics that are used to

identify an employee’s morale in the organization

Signals- Insight into all company events, investments,

product launches

Comprehensive data-driven analysis of Peer’s Globalization and talent strategies

3838

Platform Component: University Level Insights

Identify Universities with target talent- Identify relevant universities for various

technologies/sub-technologies- Job roles mapping to university curriculums

University Analysis- Curriculum Job roles mapping- Talent supply across various Technologies/job

roles- Curriculum maturity for all technologies-Top employers and Job roles they hire for- Insights around Cost per hire- Top professor profiles

Professor Profiles- Deeper analysis of professor’s expertise- Affiliations analysis

Analysis of Global universities and professor’s expertise, curriculums at technology level

3939

Platform Component: RolodexDiscover potential candidates, predict their organizational fit and strategize how to turn them into hires

Professional Bio- Education- Professional Experience- Biography- Volunteer and Associations

Predictive Attributes to estimate fit- Hiring Fit/ OI- Odds of quitting- Openness to Relocate- Odds of Promotion in Current Role- Job Progression- Skills

Single View of the social graph- LinkedIn- Twitter - Github- Kaggle etc.

Personal InterestsUnderstand Personal Interest & Hobbies to build better relationships and understand candidates beyond just resumes

4040

Platform Component: Personality and culture analysis

Understand personality traits of your potential hires and evaluate fitments with your organization culture

Engagement Guidelines§ Identify engagement drivers and right approach

to engage a candidate

Personal Interests-Identify personal and professional interest areas- Understand drivers beyond just professional Bios

Personality Inference- Understand personality traits and insights

derived on the basis of DRAUP 26 T and Disc Models- Asses Fitment with your organization’s culture and team’s personality vector

4141

DRAUP Delivery model

PLATFORM ACCESS

BRAINDESK

BREAKFAST EVENTS

NEWSLETTER/WEBINARS

Cloud hosted application with intuitive UI, data-rich insights and visualizations

Qualitative insights and primary research led reports about emerging talent trends and deep-dive into specific locations and job roles

Access to on-demand support, custom research and executive-ready presentations

Access to closed door curated networking/working sessions with peers in the talent ecosystem

Biweekly Newsletter and Webinars covering industry leading research around emerging talent trends and shifts

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