GARP Webcast SeriesNima Safaian, ERPTrading Analytics Cenovus Energy, Inc.AUGUST 2016

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Data Science for Risk Managers and Trading Professionals

Nima Safaian, ERPNima Safaian, ERP, Trading Analytics, Cenovus Energy, Inc.

Nima Safaian leads the Trading Analytics team at Cenovus Energy. Prior to that he was a Director at Scotiabank. Mr. Safaian started his career as a Nuclear Engineer working for a major power utility and later, a major international consulting firm in Toronto, Canada. After completing his MBA at the Schulich School of Business, Mr. Safaian moved to Calgary, Canada and started his risk management career at BP’s Natural Gas and Power Trading group and later Scotiabank’s Energy and Agriculture Commodities trading group. 

In 2010, Mr. Safaian earned his Energy Risk Professional (ERP®) from the Global Association of Risk Professionals, and serves on the GARP Calgary Chapter Committee.

The Journey

Vision: • Push the boundaries of what is possible in risk

management and trading analytics, through shared learning in the area of Big Data, Machine Learning, and Data Visualization.

• This group will examine methodologies and technologies that have been very successful in area of web and social media analytics and how they can be applied to energy trading and risk management.

• Shared learning will occur through seminars, guest speakers, product demos, hands-on workshops

The Journey

• Calgary GARP Meetings focused on Data Science– Meeting 1 - “The Coming Big Data Tsunami in Energy

Market Analytics” - by Jeff Fong– Meeting 2 - “Advanced Analytics in Upstream Oil and

Gas” by Dan Lozie– Meeting 3 - “Risk Manager in the Valley Journey into

2015 Strata Conference” by Nima Safaian• Data Science for Risk Managers and Trading

Professionals (Webcast)• Hands-on Data Science for Risk Managers and

Trading Professionals

Overview

• Data Science Our Journey

• Why data science?• Decision engineering - making a case for data science that

makes sense• Data science the value proposition• Building data science capacity • Data Science Community for Risk Managers

Decision Factories● What is it that we do as risk

managers and trading professionals?

● Great HBR Article: Rethinking the Decision Factory - by Roger Martin (link)

● We are part of an ecosystem that produces decisions every day

● Wrong to assume we can not codify decision making

We are all Decision Engineers!● Moving from passive providers of

information to decision engineers

● Decision engineers influence decision making by:

○ Designing a decision making framework

○ Correctly framing problems and generating hypothesis

● Decision engineers create an ecosystem of optimum “micro-decisions”

Source: HBR - Leaders as Decision Architects

Data Science Disrupts Analytics!● Data Science is poised to lead

decision engineering

○ Scientific approach to generating ideas and testing hypothesis

○ Superior use of technology to generate and distribute information

● Data Science is a Netflix disrupter to traditional analytic approach - business model vs. technology

● Democratization of analytics and power of business people

What is Data Science?● “Data Science is an interdisciplinary field about

processes and systems to extract knowledge or insights from large volumes of data in various forms, either structured or unstructured, which is a continuation of some of the data analysis fields such as data mining and predictive analytics”- Source Wikipedia

● Data science is not only about Big Data and Machine Learning

● Data science is a rigorous way of asking the right question, generating and testing hypothesis

● Data science is about generating actionable decision that help improve organizational metrics!

Science

Data

Data Science Hype → Reality

Financial and Trading Use Cases (Examples)● Clustering and unsupervised learning to better understand

market dynamics across markets

● Outlier detection and stress scenario generation

● Random vs. structural market changes

● Bayesian belief network decision trees

Data Science - Roadmap● Solve the right problem

● Communicate to influence decisions

● Hire the right talent

● Embrace Skill diversity

● Develop an open analytic approach

● Focus on building a platform

● Collaborate with Others

Attitude is everything - think like a startup

● Solve the right problem● Organize for uncertainty and

learning

● Solve the problem that matters

● Develop metrics that matter

● Validated learning - build, measure, learn cycle

● Small batch approach

● Platform/capability that enables rapid iteration cycle

Communicate to Influence● Communicate to

Influence Decisions● Risk/reward decisions

are the only metric that matters

● Embrace the power of web

● Goodbye to dashboard/reports influence through appification and interactive reports

Develop Skills - Profile of a Data Scientist

● Business knowledge - sources of risk and reward

● Entrepreneurial approach

● Storyteller/infographics

● Statistics/ML

● Coding

Image Source: http://www.oreilly.com/

Build Internally - Embrace Skill Diversity● Business vs. DS

expertise

● Develop in-house

● Avoid “Business” vs. “Developer” trap - value generalist

● No armchair Risk Managers - hackers wanted!

Image Source: http://www.oreilly.com/

Programming and Math Skills● Statistics

○ Descriptive statistics○ Inference ○ Prediction and experimental design○ Emphasis on interpretability and model based

● Machine Learning

○ Unsupervised learning e.g. clustering, KNN○ Supervised learning e.g. Naive Bayes, SVM, Neural

Network○ Emphasis on performance and algorithm based

● Advanced scripting skills: Python, R, Scala - learn all and use them behind REST

Open Source Rules the World!● Criteria for selecting analysis

tool:

○ Large and active Community

○ Exploratory analysis capability

○ Scale up capability

● Python and R are becoming language of data science

● ~7000 open source R packages (15 million users)

● Hadoop and Spark are changing the scale of data wrangling

Data Science Platform for Beginners!● Build a right platform

● Criteria: open/flexible/scalable

● Use Linux

● Data - NoSQL/Hadoop

● Web based development

● Open analytics / ecosystem/REST

● Cloud based platform

Data Science in Real World• Legacy Systems - Build Analytics around Legacy Systems

• Data Silos – Gradual move from spreadsheets of useless data and poor analytics!

• Budget - Data Science on a budget through open source

• Change Management - Data Science is an exercise in change management

Collaboration and Community• Risk Managers can benefit greatly from sharing Ideas and

learning from others

• Data Science from a Risk Manager/Trading Professional perspective vs Vendors

• The objective is to give a small glimpse into what is possible using Data Science

• Beginning of wider community engagement

Data Science for Risk Managers and Trading Professional Series

• 5 Webinars + All Examples and Slides• Ability to provide feedback and raise questions

Sessions:

• Ingesting Data and use of APIs • Cleaning and Wrangling with Data • Exploratory Visualization• Descriptive Statistics and Building Models• Deploy the tools as an online app

Why R?

• R is one of the most popular tools in Data Scientists toolbox

• R - Open source with large user community

• Single interface and many applications

• Rich statistical and graphical library sets

My R and Platform Setup for the sessions• Microsoft Azure Data Science Cluster (Optional)• GitHub Account (

https://github.com/bigdatalib/garpyyc)• Mongo dB NoSQL Database • R and Packages • dplyr, ggplot2, mongolite, xts, purrr, stats, shiny• Rstudio IDE [www.rstudio.com]

My Contact:

GitHub: bigdatalibLinkedIn: https://ca.linkedin.com/in/nimasafaianTwitter: @nimers

Video Modules• Session 1 – https://garp.wistia.com/medias/ld1aw47zlf

• Session 2 – https://garp.wistia.com/medias/ckadzlymai

• Session 3 – https://garp.wistia.com/medias/5e9zplvadl

• Session 4 – https://garp.wistia.com/medias/agxz0utl76

• Session 5 - https://garp.wistia.com/medias/edvfdgjori

A Proposal: Data Science Risk Research Group

Create an open consortium of financial risk professionals to study the effects of Data Science technology on their profession.

For anyone who would be interested, please email datascience@garp.com.

Q & A

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About GARP | The Global Association of Risk Professionals (GARP) is a not-for-profit global membership organization dedicated to preparing professionals and organizations to make better informed risk decisions. Membership represents over 150,000 risk management practitioners and researchers from banks, investment management firms, government agencies, academic institutions, and corporations from more than 195 countries and territories. GARP administers the Financial Risk Manager (FRM®) and the Energy Risk Professional (ERP®) exams; certifications recognized by risk professionals worldwide. GARP also helps advance the role of risk management via comprehensive professional education and training for professionals of all levels. www.garp.org