This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/VAW2eDht7JA Bio: Krish Swamy is an experienced professional with deep skills in applying analytics and BigData capabilities to challenging business problems and driving customer insights. Krish's analytic experience includes marketing and pricing, credit risk, digital analytics and most recently, big data analytics and data transformation. His key experiences lie in banking and financial services, the digital customer experience domain, with a background in management consulting. Other key skills include influencing organizational change towards a data and analytics driven culture, and building teams of analytics, statisticians and data scientists. Bio: Balaji Gopalakrishnan has over 15 years experience in the Machine Learning and Data Science space. Balaji has led cross functional data science and engineering teams for developing cutting-edge machine learning and cognitive computing capabilities for insurance fraud and underwriting, telematics, multi-asset class risk, scheduling under uncertainty, and others. He is passionate about driving AI adoption in organizations and strongly believes in the power of cross functional collaboration for this purpose.

1. Building a World-Class Data Science Team
Krish Swamy, SVP, Analytics & AI
Balaji Gopalakrishnan, SVP, Machine Learning

2. Krish Swamy
Big Data Analytics and Data Science
• Passions inside of work
Taking analytics down the “last mile,”
developing world-class talent in the data and
analytics space
• Passions outside of work
Music, math, and the intersection of the two
About the speakers
Balaji Gopalakrishnan
Machine Learning
• Passions inside of work
Connecting the business problem to ML in
the “first mile”, building robust delivery
models and great data science teams
• Passions outside of work
Badminton, Wikipedia, Math Coach

3. Why a world-class data science team?
• Data science-ing ain’t easy!!
• Need for increased sophistication to extract deep insights from data and deliver high fidelity
results
• Open source comes with a sharp tip, and also lots of rough edges
• Innovate and think ahead of the curve
• Ultimately, value creation depends on close collaboration with business and IT
• Forging effective partnerships is essential

4. Data science hiring is always for a mix of skills
Domain Knowledge Math and Stats
Knowledge
Software Engg.
Skills
Traditional statistician
Technology Developer
Academician
Aspirational
Data Scientist
BI or reporting analyst

5. Key Factors: Setting up and sustaining a world-class DS team
Good Hiring Machinery Superior Data Scientist Engagement
Agile Operating Model

6. Good Hiring Machinery
• University partnerships for early talent
• Leverage networks for experienced talent
• Streamlined and consistent interview process
• Develop internal talent and provide career growth
• Work on inclusion and diversity

7. Superior Data Scientist Engagement
• Eye on the prize – results driven data science projects
• Focused research opportunities – new capabilities development
• Provide plenty of opportunities to upskill
• Opportunities to share work – teach, present, publish, patent
• Access to resources – data, tools, technologies, environments,…

8. Agile Operating Model
• Agile PODs for rapid experimentation
• Collaborative, self-organizing, cross functional
• Complete ownership of tactical decisions within a project