The issues I've heard from a few people in hiring is that there is a surplus of junior data scientists from these camps and a shortage of senior data scientists to manage them. Problems not dissimilar to tech hiring in general, but companies need a lot more SWEs than data scientists.

Depends what the company is doing.

Most companies are going to utilise ML to some extent. Once technology and tooling improves they'll need boots on the ground engineers and not labs with R&D teams

Agreed

I'd like to offer a counterpoint. I attended one of the machine learning bootcamp mentioned above, and it was transformative for me. I got hired within a month, doubled my salary to over 100k, and landed a job that I enjoy and find intellectually stimulating. All this while having little to no technical experience (only math I took in college was intro to stats, and my pre-bootcamp career was in a non-technical capacity).

I completely understand why there is such a stigma around bootcamps. Nobody can deny that they don't afford the same depth that you'd get at a "real" program. But they can be amazing for career switchers like me, who had no real direction in college. Don't look down your nose at them.

Too bad ML as a service is already largely cornered by $FANG

I don't deny that knowing the math / theory is useful, but wonder if we sometimes overestimate the degree to which it is essential. For example, backprop with SGD is a good foundation for many, many, many applications of NN's, and pre-built implementations exist that let you use the technique without understanding the details of the math. And with those tools, you can experiment with many different combinations of features, different architectures, etc.

Of course understanding the theory will be helpful in knowing which architectures are most likely to be productive and what-not, but this whole field is very empirical anyway. So if your experimenting is a little less guided my intuition rooted in theory, that's not exactly the end of the world.