Bayesian thinking in work and life, ad attribution models and A/B testing, machine learning@Foursquare - Max Sklar - the data scientist show050

The Data Scientist Show

Max Sklar is an independent engineer and researcher. Previously, he was an engineering and Innovation Labs Advisor at Foursquare after 7 years at the company as a machine learning engineer. Previously, he has worked on Ad Attribution, recommendation engine, ratings. He is the host of The Local Maximum podcast. Max studied CS from Yale, and holds a Master degree in information systems from New York university. If you like the show subscribe to the channel and give us a 5-star review. Subscribe to Daliana's newsletter on www.dalianaliu.com/ for more on data science.

Daliana's LinkedIn: https://www.linkedin.com/in/dalianaliu/

Daliana's Twitter: https://twitter.com/DalianaLiu

Max's Linkedin: https://www.linkedin.com/in/max-sklar-b638464/

Max’s website: localmaxradio.com/about

Interviews he mentioned during the podcast:

Andrew Gelman, Statistics at Columbia University

Shirin Mojarad on Causality

Johnny Nelson on Free Speech and Moderation online

Stephanie Yang talking about Foursquare's Venue Rating System

Dennis Crowley: on Labs, on Innovation

Sophie Carr (Bayesian Mathematician)

Will Kurt (Bayesian)

Marsbot for Airpods

Other Episodes Mentioned

Bayesian Thinking

P-Hacking

Interview on Learn Bayesian Statistics

Highlights:

(0:00) Intro

(00:01:23) from computer science to machine learning

(00:05:35) Bayesian methods in rating system

(00:14:53) how to choose a Bayesian prior

(00:20:10) how to deal with p-hacking

(00:26:57) causality model in ad attribution

(00:35:20) Bias-correction methods

(00:45:43) negative lift in advertising

(00:51:05) unexpected consumer behaviors

(00:52:08) why he decided not to climb the "engineer ladder"

(00:56:46) the challenges of having 5 managers in a year

(01:01:38) using the 3rd-party software vs building his own

(01:04:18) how he approaches ML problems

(01:07:51) his tech stack

(01:09:25) his advise on learning machine learning

(01:12:40) projects he is working on

(01:17:10) Bayesian for his life decisions

(01:22:00) how writing helps him

(01:23:48) the confusion, stress and excitement in his career