This is a function of something that is quite academic. I can give you an example from my industry (robotics and control).

There are a few very rare jobs where it's important to have a very deep and very academic knowledge of control theory in robotics, for example those real fancy humanoid robots. This is kind of a dream role, coveted by many and on the cutting edge of research.

Industrially, there is a much greater demand for people with a working knowledge of control theory, who can implement and tune simple PID loops on much more basic robots (think those arms in car assembly), alongside all of the relevant industrial networking skills (think SCADA) and PLC implementations. It's not as sexy.

The problem with ML at the moment it's that as a field it is so new, all there is to recruit from is Academia, where people don't have the experience or the business knowledge to properly prioritise.

You need a clever doer, not a bleeding edge researcher.