Your argument reads as, “data scientists only know machine learning! Not me, I use popular models from statistics too!” Not trying to sound offensive, but it’s

Your argument reads as, “data scientists only know machine learning! Not me, I use popular models from statistics too!” Not trying to sound offensive, but it’s the same aggressive stance repeated on twitter by every other stats/econ phd. And it’s hardly ever true at any big tech company, where most data scientists (especially those writing the design docs) have PhDs in fields ripe with popular non-ml methods. I’ve seen far more e.g. fixed-effect models in production than neural networks. Far more synthetic controls and d-in-ds in research papers than ml models. Far more survey sampling methodologies and power studies than multi-armed bandits. All from the “very few” data scientists who apparently haven’t heard of these methods. There’s a big difference between data scientists working in industry (at least what I’ve seen) and the “learn ml in 10 minutes!” blog post authors on medium.