Today we’re joined by Sean Taylor, Staff Data Scientist at Lyft Rideshare Labs.
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We cover a lot of ground with Sean, starting with his recent decision to step away from his previous role as the labs director to take a more hands-on role, and what inspired that change. We also discuss his research at Rideshare Labs, where they take a more “moonshot” approach to solving the typical problems like forecasting and planning, marketplace experimentation, and decision making, and how his statistical approach manifests itself in his work.
Finally, we spend quite a bit of time exploring the role of causality in the work at rideshare labs, including how systems like the aforementioned forecasting system are designed around causal models, if driving model development is more effective using business metrics, challenges associated with hierarchical modeling, and much much more.
Connect with Sean!
- Paper: Adapting Neural Networks for the Estimation of Treatment Effects
- Paper: Variance-Weighted Estimators to Improve Sensitivity in Online Experiments
- Paper: Randomized experiments to detect and estimate social influence in networks (Complex Spreading Phenomena in Social Systems)
- Paper: Active Matrix Factorization for Surveys
- Paper: Forecasting at Scale
- Prophet | Forecasting at scale