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 Aztec 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. Follow Daliana on https://twitter.com/DalianaLiu for more on data science and this podcast. Damien's Linkedin: https://www.linkedin.com/in/damienbenveniste/
See highlights below: 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
