A PhD isn't even a requirement for doing the more advanced stuff. Obviously you need a lot of math and ML specific knowledge but there's no reason why you can't have that knowledge with an undergraduate math degree (for example). Spending 3-6 years doing research in a very narrow and possibly unrelated branch of mathematics will give you a PhD, but the linear algebra and multivariable calculus that you actually need for the ML stuff are covered in a bunch of undergrad/masters courses in mathematics, computer science, engineering, physics etc.

I second this. I have worked with a bunch of undergrads (I am pursuing a masters degree in CS) and they had a thorough grasp of the math and could really contribute to the research agenda of the group. When I did my undergrad (in 2011-15), I ended up taking a lot of electronics/hardware courses. Turns out undergrads these days just swap them with math/machine learning courses. Good for them.

I would say a PhD (or masters degree minimum) is required if you are developing new novel architectures, but not to iterate on existing architectures for a particular application.

Applied Data Science does not require developing as yet unseen architectures, being able to read cutting edge papers and applying the research is more than likely to enough.