Vin Vashishta is a chief data officer and AI strategist at V Squared, a company he founded in 2012 that provides AI strategy, transformation, and data organizational build-out services.
He teaches data professionals about strategy, communications, business acumen, and applied machine learning research methods. Vin has 130k+ followers on Linkedin talking about AI, analytics, and strategy. His website: https://www.datascience.vin/ 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
Highlights:
(0:00) Intro
(00:03:37) "ML strategy" with 'pricing' as an example
(00:09:45) what is a good metric for ML
(00:13:16) how to translate a business problem into a data problem
(00:23:42) leverage users in the "Human Machine Teaming"
(00:48:22) how he earned the trust
(01:17:31) data science evolution from 2012 to 2022
(01:31:06) how he learns new domain knowledge
(01:36:25) the mistakes he made
(01:42:15) what he learnt from his mentor
View all episodes
By Daliana Liu
4.7
7575 ratings
Vin Vashishta is a chief data officer and AI strategist at V Squared, a company he founded in 2012 that provides AI strategy, transformation, and data organizational build-out services.
He teaches data professionals about strategy, communications, business acumen, and applied machine learning research methods. Vin has 130k+ followers on Linkedin talking about AI, analytics, and strategy. His website: https://www.datascience.vin/ 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
Highlights:
(0:00) Intro
(00:03:37) "ML strategy" with 'pricing' as an example
(00:09:45) what is a good metric for ML
(00:13:16) how to translate a business problem into a data problem
(00:23:42) leverage users in the "Human Machine Teaming"
(00:48:22) how he earned the trust
(01:17:31) data science evolution from 2012 to 2022
(01:31:06) how he learns new domain knowledge
(01:36:25) the mistakes he made
(01:42:15) what he learnt from his mentor
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