Damien Benveniste is a data scientist and software engineer. Previously, he was a machine learning tech leader and mentor. He has worked for almost ten years in different machine learning roles in different industries such as AdTech market research, e-commerce and health care. He has a Ph.D. in physics from Johns Hopkins University and now working towards co-founding own startup in employee engagement space. We talked about his career journey, how he solved challenging problems, and his advice for new data scientists and engineers. 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
Damien's Linkedin: https://www.linkedin.com/in/damienbenveniste/
(00:00) Intro
(00:01:17) from quantitative trading to machine learning
(00:07:52) his experience at Meta
(00:21:16) automated machine learning
(00:28:52) model paradigm
(00:32:47) the productivity-oriented culture at Meta
(00:41:42) short-term gain vs long-term goal
(00:44:38) things he liked at Meta
(00:51:54) the project that shaped his career
(01:03:56) the importance of having a baseline for ML models
(01:09:12) why he time-boxed everything
(01:16:25) test the model in production
(01:20:05)experimental design for ML
(01:23:25) the most challenging project he worked on
(01:37:07) best practices for machine learning
(01:48:44) how he sees himself
(02:00:52) lessons he learnt from being layoff
(02:06:45) frustration he had in his previous job
(02:16:14) what he is working on
(02:29:18) the future of machine learning
(02:39:52) things he is excited about
View all episodes
By Daliana Liu
4.7
7575 ratings
Damien Benveniste is a data scientist and software engineer. Previously, he was a machine learning tech leader and mentor. He has worked for almost ten years in different machine learning roles in different industries such as AdTech market research, e-commerce and health care. He has a Ph.D. in physics from Johns Hopkins University and now working towards co-founding own startup in employee engagement space. We talked about his career journey, how he solved challenging problems, and his advice for new data scientists and engineers. 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
Damien's Linkedin: https://www.linkedin.com/in/damienbenveniste/
(00:00) Intro
(00:01:17) from quantitative trading to machine learning
(00:07:52) his experience at Meta
(00:21:16) automated machine learning
(00:28:52) model paradigm
(00:32:47) the productivity-oriented culture at Meta
(00:41:42) short-term gain vs long-term goal
(00:44:38) things he liked at Meta
(00:51:54) the project that shaped his career
(01:03:56) the importance of having a baseline for ML models
(01:09:12) why he time-boxed everything
(01:16:25) test the model in production
(01:20:05)experimental design for ML
(01:23:25) the most challenging project he worked on
(01:37:07) best practices for machine learning
(01:48:44) how he sees himself
(02:00:52) lessons he learnt from being layoff
(02:06:45) frustration he had in his previous job
(02:16:14) what he is working on
(02:29:18) the future of machine learning
(02:39:52) things he is excited about
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