What "maths" is keras? Or scikit-learn?
For what it's worth, to understand scikit-learn doc/tutorial I'd say you'll need Probability, Linear Algebra, Multivariate Calculus and, yeah, Stats.
Not necessarily at a PhD level but still. And more you understand maths farther you can get in AL/ML.
These libraries leave most of the actual day to day work for ETL. ETL happens to be highly data and problem dependent, so it can't be easily automated or reused. For this reason I think the best thing to be a good applied ML person is a solid programming background. You should have a working knowledge of statistics and linear algebra, but the most useful skill really is being able to write good code. It's different for research of course.
Those are ML and AI frameworks that use a tremendous amount of mathematics under the hood, but you can also reliably treat them as blackbox learning systems too. Understanding the model generation procedure and setup is often unneeded. And many tools will help direct you toward what algorithms makes the most sense for your data, and even have competitions to figure out which actually works best. I agree, it's a little disappointing, but admittedly it doesn't take a PhD to do this stuff anymore.
It is important to note that just because you can do all the stuff a PhD Scientist might regularly do, doesn't mean that someone will hire you for it. In that case you might need to have a PhD in mathematics, computer science or a related field. But that is more a consequence of competition and long term talent investment, than the practice of ML/AI itself.
Competition (labor supply side) and ultimate success of current ML approaches.
As the market starts to overheat, it seems that there will be a labor shortage/good quality workers will be scarce and we'll have to make simple tools for simpletons. But this is all a huge "if". Eventually the market will contract a lot and slack labor market conditions will have companies hiring them PhDs.
