I think most of this is due to the fact that most ML techniques need lots of specific knowledge to apply correctly. Many introductions focus too much on specific algorithms, for example, while ensemble methods are probably best in real-world data. The pactices of building training and test sets, of regularizing, of doing proper feature engineering, and of making structured models (for example) are more easily learned in a mentor-student relationship than by reading blogs (reading papers might take you a long way, but you won't build an intuition as to why things work and why they fail without lots of experimentation or advice from someone who already has this intuition) and/or AI books, and ML books are usually far too technical (or too superficial in the technical side, as is programming collective intelligence).

> I'm sure CS PhD's are in good shape but breaking into ML from another programming discipline seems pretty hard.

That's because ML isn't a "programming discipline". It's pretty much pure statistics, optimization algorithms, and linear algebra these days, and those algorithms are HARD to code, HARD to scale.

This is true - my boss has been pressuring me to do the legwork required to get at least a masters in stats or CS so I can carry a higher title in our institutional research department.

Just for those outside of academia - state schools really drink their own Kool-aid. Associate/assistant directors require a masters degree in anything while directors require PhD credentials.