> I typically end up using a lightGBM model for pretty much everything at the end of the day I laughed a bit when I read this. This matches my experience exac

> I typically end up using a lightGBM model for pretty much everything at the end of the day I laughed a bit when I read this. This matches my experience exactly. Most of the crap I do daily is basically the ML equivalent of CRUD apps. Oh we need to make some data-driven decision here to optimize this metric. Typically any basic ML, especially something like LightGBM or CatBoost, works so well it's pointless trying to squeeze out a little more performance. And yeah, this is also normal-company shit, not MAANG stuff. There is so much low-hanging fruit at normal companies. Another bonus is that you get to do full-stack DS, from understanding the business problem, gathering the data, building the solution, getting it to production and showing the result. I feel like I get to keep up to date with pretty much all the dev tooling, leaving an exit back to software dev if things go to shit (I was full-stack SWE about 7 years ago), or just continuing to specialize in statistics and ML (currently applying mixed-effect models and survival analysis in a domain that no one has probably thought of applying it to).