Netflix Data Science: A confluence of metrics, algorithms, and experimentation
Whether it's personalizing recommendations of movies and TV shows or optimizing the streaming of video bits to peoples' households, Netflix relies heavily on data science techniques. We believe in continuous learning through predictive modeling & algorithms, experimentation, and principled metric design. This talk will highlight Netflix's core data science strategies and uses, with particular focus on our successes and challenges in experimenting with personalization algorithms.
Caitlin Smallwood is VP of Science and Algorithms at Netflix, where she and her team focus on predictive models, algorithms, and experimentation science for all parts of the Netflix business. With a background in Internet products (Netflix, Yahoo!, Intuit) and mathematical consulting (PwC, SRA), Caitlin specializes in analytics, recommendation systems, controlled experiments, product management, and data strategy. Caitlin holds a M.S. in Operations Research from Stanford University and a B.S. in Mathematics from The College of William and Mary.