Tangentially, I also think the term data scientist has been so abused as to almost be meaningless at this point.
When I was applying for jobs it could range from anything from "knows how to use MS Excel" to "Can train large language models at scale".
Personally I went for ML Engineering. My company at some point hired people as data scientists (some of my more senior colleagues still have the title, despite doing the same work I do), but started hiring people as ML engineers, i.e. people who can do half-decent SW engineering and also do ML. Just a filtering thing I guess.
I have a suspicion the term will start to fall out of fashion as things become more specialised.
I've run a "data science consultancy" in some form or fashion for three years now.
When people say "data science" they mean one of three things:
(1) MLE
(2) Data Management
(3) Data Analysis or Business Intelligence (applications of the same skillsets).
(1) has a lot of ongoing innovation, be it in MLOps, autoML, mapping frontier ML to business cases, etc. Innovation is expensive if the investment strategy is unprincipled. (2) is a critical and essential part of making data a usable asset. Management is expensive if it exists solely as a control process and gatekeeps access and use. (3) is core and will never get away from the adhocs and the standard flows, but the inferences are often dubious or not logically justifiable and requires depth of statistical knowledge (rare) to do well -- and courage to call out BS.
Very few people have the depth to do all three. What I have found is that many businesses hope for capacity in all three, plus some basic SWE, in the hope that they can decrease labor expenses. Not an irrational hope, to be frank, but ultimate the iron law of business holds: you can have it good, fast, or cheap -- pick two and be happy with one.
My core observation (and one I see validated based on client interest and experience) is that this is not new and has happened before -- it is the hype cycle in action. The digitization process (including moving to digital and then moving to Web) had a similar cycle. When you treat "data science" like its a silver bullet it will generally fail to do anything but suck budget. When you embed it with your technology teams and treat it as an iterative add, as useful as devops, etc., you have a better chance for value add.
I've found three core customer sets that helped us define a sustainable business:
(1) government agencies (which tend to put most expenditure under labor categories, so they hire a lot of long-term consultants and contractors)
(2) mid-to-small sized non-technology firms that want better data science strategy or want to build data-driven features into applications/products (especially in novel ways)
(3) smaller technology companies that don't have the MLE and data management system capabilities.
My career has been in heavily regulated industries, so our customers often have an appreciation for the management and governance portion after experiencing negative data science outcomes from maverick types.
wow. How you phrase the work and client expectations make seem like working for you is a breath of fresh air in the DS world. Let me know if you ever need a contractor, or let me know how to reach you with a resume :D
Personally I went for ML Engineering. My company at some point hired people as data scientists (some of my more senior colleagues still have the title, despite doing the same work I do), but started hiring people as ML engineers, i.e. people who can do half-decent SW engineering and also do ML. Just a filtering thing I guess.
I have a suspicion the term will start to fall out of fashion as things become more specialised.