This isn't to say that PHd knowledge isn't valuable but if you look at firms in finance that have had success with data i.e. RenTech, they hire very smart people with PHds but it isn't only the PHd. You need someone who has the knowledge AND someone who has common sense/can get results. That is very hard to do correctly (and yes, some people who come from academia literally do not want anything to do with business...it is like the devs who come from a CS PHd and insist on using complicated algo and data structure everywhere, optimising every line, etc.).
It's an abbreviation of the Latin "philosophiae doctor" == "doctor of philosophy".
Getting one is as much about persistence as intelligence, and makes you very knowledgeable about one narrow area, so what you say makes sense. Academic researchers usually branch out into other fields and subfields as well, but straight out of a PhD, narrow and deep is what you tend to get.
To be fair, I started to understand why developers gave out about bootcamp grads lacking a foundation when the bootcamps came for my discipline (data science).
The PhD fetish is pretty mental (even though I have one), as it's really not necessary.
Additionally, everyone thinks they need researchers, when they really, really don't.
Having worked with researchy vs more product/business driven teams, I found that the best results came when a researchy person took the time to understand the product domain, but many of them believe they're too good for business (in which case you should head back to academia).
What you actually need from an ML/Data Science person:
- Experience with data cleaning (this is most of the gig)
- A solid understanding of linear and logistic regression, along with cross-validation
- Some reasonable coding skills (in both R and Python, with a side of SQL).
That's it. Pretty much everything else can be taught, given the above prerequisites.
But it's tricky for hiring managers/companies as they don't know who to hire, so they end up over-indexing on bullshitters, due to the confidence, leading to lots of nonsese.
And finally, deep learning is good in some scenarios and not in others, so anyone who's just a deep learning developer is not going to be useful to most companies.