Uploaded September 2022 | Updated September 2026, 2 weeks ago
Damien Benveniste is a data scientist and software engineer. Previously, he was a machine learning tech leader 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 dalianaliu.com for more on data science.
Daliana's LinkedIn: linkedin.com/in/dalianaliu
Daliana's Twitter: twitter.com/DalianaLiu
Damien's Linkedin: linkedin.com/in/damienbenveniste
0:00 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-metric-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 when develop ML model
01:09:12 why he time-boxed everything
01:16:25 test the model in production
01:20:05 experimental design for machine learning
01:23:25 the most challenging project he worked on
01:37:07 best practice for machine learning
01:48:44 how he sees himself
01:55:01 "controversial" opinions
02:00:52 lessons 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
Damien Benveniste is a data scientist and software engineer. Previously, he was a machine learning tech leader 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 dalianaliu.com for more on data science.
Daliana's LinkedIn: linkedin.com/in/dalianaliu
Daliana's Twitter: twitter.com/DalianaLiu
Damien's Linkedin: linkedin.com/in/damienbenveniste
0:00 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-metric-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 when develop ML model
01:09:12 why he time-boxed everything
01:16:25 test the model in production
01:20:05 experimental design for machine learning
01:23:25 the most challenging project he worked on
01:37:07 best practice for machine learning
01:48:44 how he sees himself
01:55:01 "controversial" opinions
02:00:52 lessons 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
