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Bayesian thinking in work and life, ad attribution models and A/B testing, machine learning@Foursquare - Max Sklar - the data scientist show050
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
View all episodes
By Daliana Liu
4.7
7575 ratings
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
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