Where to Find Sean J. Taylor's Talks, Podcasts, Newsletter, and Social Profiles

Sean J. Taylor's site, seanjtaylor.com, is a personal hub that links out to his public content and profiles. The homepage itself hosts little original material; instead, it points visitors to a newsletter signup, a blog, his startup, and a list of talks and podcast appearances. The links below are the ones listed on the site, so you can use this page as a single starting point rather than searching each platform separately.

Talks and Podcasts Listed on the Site

The "Videos and Podcasts" section collects interviews and conference talks, mostly on experimentation, causal inference, and data science practice. The entries named on the page are:

• What's New in Data? podcast with John Kutay
• "When not to use SQL" — a talk at NormConf
• "Minimum Viable Experimentation" on the Analytics Engineering Podcast
• "Causal Inference and Sequence Data" on the Super Data Science podcast
• "The Relationship between Experimentation and Causal Inference" — a talk at the Nubank Data Science meetup
• "Causal Inference Approach to Matching in Two-Sided Marketplaces"
• An interview on The Data Exchange Podcast with Ben Lorica and Jenn Webb
• "When do we actually need Causal Inference" — a talk at the New York Open Statistical Programming Meetup
• A TWIML interview on causal models in practice with Sam Charrington
• An interview on the Gradient Dissent Podcast with Lukas Biewald
• A Learning Bayesian Statistics podcast episode with Alex Andorra
• A keynote, "In Defense of Curve Fitting," at the 2020 Causal Data Science Meeting
• A short talk about Propensity (the listing is truncated on the page)

If you want a single overview of how he frames experimentation and causal methods for practitioners, the NormConf talk and the Analytics Engineering Podcast episode are the most directly relevant to applied work.

Newsletter, Blog, and Social Profiles

The homepage's "useful links" list is the place to go for ongoing updates and contact:

The site does not publish a contact email address or a public posting schedule, so the newsletter and social profiles are the practical ways to follow new work.

Background in Brief

The "Background" section explains why the content leans toward causal inference and business decisions. He has been co-founder and chief scientist at Motif Analytics since 2022; before that he was a data scientist and manager on the Rideshare Labs team at Lyft (2019–2022) and a research scientist and manager on Facebook's Core Data Science Team (2012–2019). He earned a Ph.D. at NYU's Stern School of Business (2008–2013), with a dissertation on social influence from online social signals advised by Sinan Aral. Earlier roles include software engineer at Matrix Group, International, and research assistant at the Federal Reserve Board.

The site also groups his interests under Data Science, Social Science, and Experimentation and Statistics, which map onto the talk topics above. If your goal is to find a specific talk, start with the Videos and Podcasts list; if you want to track new writing or work, the newsletter and the linked profiles are the active channels.