Given that talent is our focus, we know that hiring highly talented data scientists can drastically impact delivering significant business value out of your analytics team. However, to get the most out of that talent and achieve optimal business success, it’s critical to structure your team with the right roles, empower them with the right responsibilities, and continuously challenge/develop them for on-going engagement and motivation.

Join us over the next 2 months as we discuss a series of 5 pitfalls we’ve seen data science teams encounter and how you can avoid or work around them to achieve the best results.

Part 2: Thursday, January 17th at 1 PM Central Time

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The second webinar will feature a panel discussion between Raja Chakarvorty, the VP of Data Science at Protective Life, Jacob Kosoff, the Head of Model Risk Management & Validation at Regions Bank, and Nathan Black, the Chief Data Scientist at QuantHub. They will be sharing some of their wisdom around structuring analytics organizations, organizing teams and implementing processes to encourage collaboration, and what skills/roles they think are most important to create a high functioning data science/analytics team. A few topics we will cover:



Structuring an analytics organization

Skill coverage and team organization

Determining what roles are needed

P roduction deployment, management, and team support

Experience with results and the models chosen

Raja Chakarvorty is a Data and Analytics leader with over a decade of experience in the Insurance domain developing and implementing Data and Analytics strategy. He has delivered numerous data science projects with a significant positive impact on top and bottom line. Raja believes in building institutions and contributing to organizations by providing data-driven solutions to complex business problems. Raja’s niche is to build Data Science team and foundational capabilities for organizations to support data science program and generating a positive ROI. In his current role, Raja heads the Data Science program at Protective Life. He has previously worked as the Head of Personal Lines Data Science at Hanover Insurance Group and as a Manager of Research & Analytics at Travelers Insurance. Raja holds a PhD in Physics from the University of Notre Dame.

Jacob Kosoff is the Head of Model Risk Management and Validation (MRMV) at Regions Bank, serving in this role since May 2014. As the Head of Model Risk Management and Validation, Jacob is responsible for the management of the model governance and model validation teams and for overseeing the governance and validation for all models and analytical tools at Regions Bank. Jacob served as the Model Governance Manager at Regions prior to his current role.

Prior to Jacob’s time at Regions, Jacob served in multiple leadership roles at PNC in Pittsburgh, PA for 4½ years, including in Credit Review and Model Risk Management. Prior to Jacob’s time at PNC, Jacob served as a model developer and senior economic analyst at Freddie Mac in McLean, Virginia for 3 years. Prior to Jacob’s time at Freddie Mac, Jacob worked for 3 years at Genesis Analytics as an economic analyst and consultant, developing and implementing models for various banking clients in South Africa. Jacob has also served as a lecturer in the School of Economics and Business Sciences for 3 years at the University of the Witwatersrand, Johannesburg.

Jacob has a Master of Commerce Degree in Economics from the University of the Witwatersrand and Bachelor of Science with Honors in Economics from Pennsylvania State University. Jacob currently serves on the Board of Directors for the Levite Jewish Community Center (LJCC) in Birmingham, AL. Jacob resides with his wife and three children in Birmingham, Alabama.

Nathan Black is a Data Science Professional and AI Researcher with over 5 years of experience leading and working alongside quant teams to develop cutting-edge, end-to-end data solutions in manufacturing, healthcare, food retail, finance, and education industries. Nathan has a proven track record of using data to help people thrive, assisting organizations in capturing value from data and technology through the deployment of BI, Prescriptive Modeling, and Artificial Intelligence applications.

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Upcoming and Past discussions:

Completed – Part 1: Failing to align the data science priorities with the needs of the business. Recap available here.

Part 3: Insufficient validation of models and methodology

Part 4: Lack of engagement and infrequent activation of the inner problem-solver

Part 5: Inadequate training opportunities to keep skills sharp and relevant

Be sure to save your seat! Spots are limited so we can keep it interactive.

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