The problem is DS is really 2-3 different disciplines under one nebulous title. What you're describing is folks who are prototyping and productionizing models. That's definitely in short supply, but random STEM PhDs are in no way competitive for those roles unless they're coming from CS programs + have work experience in production engineering.
But that's by no means all of the DS field. There are lots of DS jobs where you're collecting and interpreting and communicating about complex data sets. An engineering mindset is occasionally helpful, but a bias towards building versus towards analyzing and writing can just as often be counter-productive. Not all problems are solved by systems; lots of problems are solved by better understanding the problem and then letting other specialists build the right solution.
The bootcamps have contributed to the problem by focusing so much on building things. The idea that you can go from an econ undergrad to being a self-sufficient member of a production ML team in 6-12 weeks is nuts. What's less nuts (and what I wish programs like Insight focused on) is taking people from having data skills in one domain and with one set of tools (e.g. logitudinal medical record data, stored in CSVs and handled in Stata) to another set of tools (billions of rows of event-based product data stored in a data warehouse, processed in R or Python). But instead the bootcamps behave like the missing skillset is the ability to make a predictive random forest model on some arbitrary data set and build an AWS web app around it. THAT job market definitely doesn't exist and is completely over-saturated.
But people who are smart communicators about data, can manipulate and make sense of massive data sets, can ask incisive questions about their data, and can use data to convince people of a complex argument are always going to have job opportunities, even if they're not production grade engineers. If that sounds like you, I'm hiring - hit me up on Twitter: @drewwww.