Episode 33

#33 Bayesian Structural Time Series, with Ben Zweig

How do people choose their career? How do they change jobs? How do they even change careers? These are important questions that we don’t have great answers to. But structured data about the dynamics of labor markets are starting to emerge, and that’s what Ben Zweig is modeling at Revelio Labs.

An economist and data scientist, Ben is indeed the CEO of Revelio Labs, a data science company analyzing raw labor data contained in resumes, online profiles and job postings. In this episode, he’ll tell us about the Bayesian structural time series model they built to estimate inflows and outflows from companies, using LinkedIn data — a very challenging but fascinating endeavor, as you’ll hear!

As a lot of people, Ben has always used more traditional statistical models but had been intrigued by Bayesian methods for a long time. When they started working on this Bayesian time series model though, he had to learn a bunch of new methods really quickly. I think you’ll find interesting to hear how it went…

Ben also teaches data science and econometrics at the NYU Stern school of business, so he’ll reflect on his experience teaching Bayesian methods to economics students. Prior to that, Ben did a PhD in economics at the City University of New York, and has done research in occupational transformation and social mobility.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege and Rémi Louf.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

About the Podcast

Show artwork for Learning Bayesian Statistics
Learning Bayesian Statistics
A podcast on Bayesian inference - the methods, the projects and the people who make it possible!

About your host

Profile picture for Alexandre ANDORRA

Alexandre ANDORRA

Hi! I'm your host, Alex Andorra. By day, I'm a Bayesian modeler at the PyMC Labs consultancy. By night, I don't (yet) fight crime, but I'm an open-source enthusiast and core contributor to the awesome Python packages PyMC and ArviZ.

An always-learning statistician, I love building models and studying elections and human behavior. I also love Nutella a bit too much, but I don't like talking about it – I prefer eating it.

My goal is to make this podcast as interesting and useful to you as possible. So, hit me on Twitter or email with your questions and suggestions!