What Is seanjtaylor.com?

Updated

seanjtaylor.com is the personal website of Sean J. Taylor, a data scientist, social scientist, statistician, and software developer. The site serves as a professional profile: it summarizes his background, lists talks and podcast appearances, and links out to his startup, newsletter, GitHub, LinkedIn, Google Scholar, Twitter, and an infrequently updated blog. It is not a product, service, or blog with regularly published articles — it is a hub page for his work and public appearances.

Who the site is about

The homepage describes Taylor as a generalist who specializes in methods for causal inference and business decision problems, with an interest in building tools for practitioners working on real-world problems. It notes collaborations across computer science, economics, political science, statistics, machine learning, and business school research.

The page organizes content under these headings:

  • Background — a chronological career summary
  • Videos and Podcasts — a list of talks and interviews
  • Data Science
  • Social Science
  • Experimentation and Statistics

Career background listed on the site

Period Role
2022–present Co-founder and chief scientist at Motif Analytics (his startup)
2019–2022 Data scientist and manager, Rideshare Labs team at Lyft
2012–2019 Research scientist and manager, Core Data Science Team at Facebook
2008–2013 Ph.D. student, NYU Stern School of Business, Information Systems; dissertation "Social Influence from Online Social Signals," advised by Sinan Aral
2006–2008 Software engineer at Matrix Group, International
2004–2006 Research assistant at the Federal Reserve Board
2000–2004 Undergraduate at The University of Pennsylvania (Economics, Finance, Information Systems)

What you can find there

The Videos and Podcasts section links to appearances including:

  • "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"
  • Interviews on The Data Exchange Podcast, TWIML, Gradient Dissent, and Learning Bayesian Statistics
  • A keynote, "In Defense of Curve Fitting," at the 2020 Causal Data Science Meeting

The page also points to external profiles (Twitter, GitHub, LinkedIn, Google Scholar) and a newsletter signup.

When this site is useful to you

  • You want a quick professional profile of Sean J. Taylor — the Background section gives a dated career timeline in one place.
  • You are looking for his talks or interviews on experimentation, causal inference, or data science practice — the Videos and Podcasts list is the main index.
  • You want to follow his current work — the site links to Motif Analytics, his newsletter, and social profiles.

If you need regularly updated writing, note that the site itself describes the blog as infrequently updated; the linked newsletter and external profiles are the better places to check for new material. The page does not list pricing, services for hire, or contact details beyond the linked profiles.