I don't think I'm underestimating "people with formal training in graduate level statistics", but quite possibly the particular people you think of when you say

I don't think I'm underestimating "people with formal training in graduate level statistics", but quite possibly the particular people you think of when you say the quoted phrase. I've worked with stats/comsci/bio-informatic/ML PhDs at multiple multi-billion-dollar software companies now and it's certainly not true that even the majority of people with graduate stats training are excellent software engineers. Would love to work at a company where that's true, I just don't think that's the common case at all. > The skill set really is extremely different from general backend engineer with a proclivity for hacking around ML models and also is really different from “research developer” who rarely deals with end to end systems or necessary concerns of production or quality code factoring, and also is super different from data science which is effectively just ad hoc business analytics but with more impressive pedigree on the resume. Totally agree. The terms are very messy though. The "ML Engineers" with PhDs in ML are still called "Data Scientists" at Zendesk, for example. At other companies "Data Scientist" just means 'Data Analyst that knows SQL'. > I’d define a machine learning engineer... Would be happy with this definition. It's much clearer than what you can gather from Lyft's post. It's close to the definition I currently hold to. On this definition I've enrolled in graduate Maths+Stats as I'm still almost entirely a Data Engineer / Backend Engineer in terms of skillset.