I'm definitely not denying the worth of the particular skillset, just like of good DBAs. But as with any skillset, there are diminishing returns. You can get fairly easily someone at the point where they can merge data from multiple sources, create automatic reports with graphs, make simple similarity clustering, regressions and expert systems even if in suboptimal ways and most companies don't really need more than that. They can even learn to integrate cloud/black box solutions for image/speech recognition without having any idea of how to write one from scratch.

Of course, if a company wants to truly innovate in the area it will need PhDs or people with great dedicated knowledge in ML/Statistics/Particular Domain, if it needs to scale it will need good data engineers to create the data pipeline together with DBAs and experts in each tools (like Spark/Flink), but for most companies the basic above is already a great improvement to what they had before.