This talk will explore how time - and timing - impact Netflix’s data science applications. Netflix enables stories from around the world to be discovered, enjoyed, and shared globally. Success in this space means finding and creating the right movies and TV shows, connecting them with the cultures and people who will enjoy them, and delivering a high-quality video streaming experience. As we lean on data science to help in these areas, the specific manner in which time is handled can impact results dramatically. I will share examples of how Netflix incorporates time in algorithm training, feature engineering, A/B test execution, and measurement of member behaviors.

Speaker Bio

Caitlin Smallwood is the Vice President of Science and Algorithms at Netflix, where she and her team drive predictive decision models, algorithm / machine learning research, and experimentation science for all parts of the Netflix business. Caitlin is particularly passionate about personalization and other mechanisms of providing value to people through data.

Prior to joining Netflix in 2010, Caitlin worked at Intuit, Yahoo!, and several consulting firms (PwC, SRA), focusing on an array of analytic disciplines and products. She is experienced at leading strong teams and has built several data science groups from scratch. Caitlin holds an M.S. in Operations Research from Stanford University and a B.S. in Mathematics from The College of William and Mary.