# Sean Taylor — Business Decision Problems

Page: https://stenobird.com/podcast/gradient-dissent/sean-taylor-business-decision-problems
Text version: https://stenobird.com/podcast/gradient-dissent/sean-taylor-business-decision-problems.md
Podcast: [Gradient Dissent: Conversations on AI](https://stenobird.com/podcast/gradient-dissent)
Published: 2021-05-13T19:00:00+00:00
Episode link: https://wandb.ai/site/resources/podcast
Audio file: https://podcasts.captivate.fm/media/716d606c-3b4d-4846-b940-cd10fb490a8b/1041905833-wandb-gd-sean-taylor.mp3
Processing state: failed
JSON: https://stenobird.com/v1/public/podcasts/gradient-dissent/episodes/sean-taylor-business-decision-problems
Duration seconds: 2741

## Resource

Sean joins us to chat about ML models and tools at Lyft Rideshare Labs, Python vs R, time series forecasting with Prophet, and election forecasting. --- Sean Taylor is a Data Scientist at (and former Head of) Lyft Rideshare Labs, and specializes in methods for solving causal inference and business decision problems. Previously, he was a Research Scientist on Facebook's Core Data Science team. His interests include experiments, causal inference, statistics, machine learning, and economics. Connect with Sean: Personal website: https://seanjtaylor.com/ Twitter: https://twitter.com/seanjtaylor LinkedIn: https://www.linkedin.com/in/seanjtaylor/ --- Topics Discussed: 0:00 Sneak peek, intro 0:50 Pricing algorithms at Lyft 07:46 Loss functions and ETAs at Lyft 12:59 Models and tools at Lyft 20:46 Python vs R 25:30 Forecasting time series data with Prophet 33:06 Election forecasting and prediction markets 40:55 Comparing and evaluating models 43:22 Bottlenecks in going from research to production Transcript: http://wandb.me/gd-sean-taylor Links Discussed: "How Lyft predicts a rider’s destination for better in-app experience"": https://eng.lyft.com/how-lyft-predicts-your-destination-with-attention-791146b0a439 Prophet: https://facebook.github.io/prophet/ Andrew Gelman's blog post "Facebook's Prophet uses Stan": https://statmodeling.stat.columbia.edu/2017/03/01/facebooks-prophet-uses-stan/ Twitter thread "Election forecasting using prediction markets": https://twitter.com/seanjtaylor/status/1270899371706466304 "An Updated Dynamic Bayesian Forecasting Model for the 2020 Election": https://hdsr.mitpress.mit.edu/pub/nw1dzd02/release/1 --- Get our podcast on these platforms: Apple Podcasts: http://wandb.me/apple-podcasts​​ Spotify: http://wandb.me/spotify​ Google Podcasts: http://wan…

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