My guess is ML scientists generally care less about statistical rigor which can lead to poor outcomes due to not properly understanding the data, assumptions, risk involved, etc

Ex: Zillow

Right. I mean, most people suck at their jobs, period though so… 🤷🏼

Most ML scientists aren’t actually fluent in the application of the algorithms they use. They have superficial understanding, they’re slow and buggy programmers, write slow code, spend months working on models that should take a few days to put together, overindex on hyperparam selection and tuning, playing with new algorithms, and don’t know how to validate their models and end up deploying garbage that often is literally no better than a coin flip. But they’re great at convincing people that they’re right on the cusp of solving a really big problem and adding a ton of value which buys them enough time to fart around for a few years and then get another job with a 30% raise and then do it all over again.