Yuling Ma – Senior Vice President of Engineering at FreeWheel

Saira Kazmi – Enterprise Data Strategy and Engineering at CVS Health

AI/ML is top of mind for leaders across academia and the industry. Many have tried and failed several times before successfully deploying models that bring real business value or have an impact. Even after a model is deployed, constant maintenance and monitoring is required to ensure that the model is relevant and makes good decisions that are still applicable to the changing business environment. Key factors leading to successful deployments include: * Availability and maturity of data to build a model and availability of this data when making real-time decisions * Thorough understanding of the business problem * Understanding of nuances leading to data variability * We can often overestimate what models can do – a simple test “Is the Candidate task simple for a human to solve?” can help. * Automation will lead to ROI (the problem is large enough to automate) * Mechanisms are in place to track the algorithm performance * There is a way to provide feedback on the solution and model decisions * The model is kept alive (refreshed with new data at regular intervals

June Andrews – Data Science Manager of Style Discovery at Stitch Fix

Vijay Pappu – Senior Machine Learning Engineer at Spotify

Vijay Pappu – Senior Machine Learning Engineer at Spotify

Tim Yoo – Sr. Director, Head of Analytics at Roku Inc

The content acquisition lifecycle is complex and multi-faceted. Streaming services that strive to be both a competitive service and a meaningful source of entertainment to consumers need to efficiently value content as well as understand the complex relationship between value to the company and to consumers. Discover how analytics and modeling can be used to navigate through the various stages of the content acquisition lifecycle.

Rochelle March – Head of ESG Innovation and Analytics at Dun & Bradstreet

Global changes have impacted countries and companies everywhere. From climate change, the Covid-19 pandemic, resource constraints and demographic fluctuations challenge the stability of even the longest-established enterprises. Traditional financial data is not enough today to provide a clear enough picture on sourcing, investment and insurance decisions. ESG data and metrics can serve as valued information and tools for competitive advantage. For Dun & Bradstreet, this means extending its efforts around business transparency to generate ESG intel that can help customers, investors and other stakeholders identify which companies are actively moving towards a different, and hopefully, more sustainable future. This presentation will showcase analysis that explores the relationship between ESG and financial performance, and will provide a deep dive into the NLP machine learning and analytical techniques used to create Dun & Bradstreet’s new ESG Rankings dataset and model.

Sriram Subramanian – Head of Data Science & Engineering at Condé Nast

Laura Gabrysiak – Data Science Manager at Visa