I have friends in the bio/medical side (phds, not MDs) who are following that trajectory too.
The (sorry) data on that is thin.
And it would hardly be the first time that a hugely promising career field has gone bust.
Over my lifetime, I've seen the technical "hot jobs" go from:
⚫ Highway and civil engineering (1960s - early 1970s)
⚫ Nuclear engineering (1970s to March 28, 1979).
⚫ Defense and aerospace engineering (1980s through 1992).
⚫ Doctor / medicine (through the mid 1980s and the rise of HMOs / "managed healthcare")
⚫ Petroleum engineering (1960s - 1980, 1999 - present, big empty hole 1980 - 1999)
⚫ Genetic engineering (mostly false starts: late 1970s / early 1980s, again early 1990s, again post dot-com bust -- but it's never really launched)
⚫ "Data Scientist" (2009 - present)
⚫ "Webmaster" (1997 - 2001)
⚫ "Mobile Apps Developer" (2007 - ~ nowish, starting to fade)
Just because you're getting your time in the sun now doesn't mean the glow won't fade.
Why does it matter that actuaries are certified? It's a professional role which requires dealing with some pretty heavy concepts. Employers presumably want to be sure that candidates meet some level of capabilities.
An actuary is someone who does actuarial work. Most people join the profession right out of college. They have no piece of paper other than their degree. They are still actuaries.
A credentialed actuary is someone who has gone through the entire educational system, which are a series of exams that are typically taken while working as an actuary. Credentials and the practice rights that come with them permit an actuary to do exactly one new thing. Only a credentialed actuary can opine on the adequacy of an insurance company's loss reserves (to make sure that the company has set aside enough money for all of the claims it will eventually have to pay).
Not many actuaries issue formal statements of actuarial opinion. All of the other work that we do can be legally done by anyone. There is nothing stopping an insurance company from hiring a large number of statisticians or whatever to do the day-to-day work, only bringing in a credentialed actuary to do the reserve opinion once a year. Companies generally don't do this because actuaries are more than just statisticians; they're business professionals with very deep domain knowledge. Credentialed actuaries command a premium in the marketplace not because of any "regulatory moats", but because the specialized education itself is very valuable. If that ever stops being the case then nothing is stopping the market from correcting itself.
That said, the business model for actuarial work typically hasn't been mining every last shred of individual and personal information (caveats, below) for retail advertising. Instead it's been measuring, tracking, and modeling risks within insurance, and many of the business lines don't concern individuals. You've got business, shipping, and industrial insurance, though yes, also life, healthcare, auto, home, and fire.
In particular, insurance is a fairly regulated market with exemptions from anti-trust collusion for the specific purpose of facilitating underwriting boards -- these are multi-company associations which pool risk data. It tends to make for a slightly less cut-throat competitive environment than IT. Also that personal data isn't bandied about quite so casually.
Though as I said, that's changing. I've been informed by industry insiders that a common practice now in automobile insurance is to purchase "smog reports" from states. These include a tremendous amount of data (most of it specific to vehicle performance), of which the salient element is the miles driven since the previous check. Turns out that your auto mileage is a significant risk predictor.
Though the fact that data gathered for one stated purpose (clean air) is being used for another (rate adjustment and fraud detection) is troubling.
I'd also point out that most of the jobs on your list are specific roles in specific industries - data science as a concept will be more resilient because it's an evolving set of skills (as is programming/software engineering) that span basically the whole economy. The day people no longer have a reason to build systems using computers is the day these jobs will go away.
Sooo, not too long after some clever person figures out how to automate 80% of the grunt work of building an analytics pipeline and their colleagues dumb down the 20% left so that the people inhabiting the C-suite can work it on their iPads...?
I know at least three startups working that space, right now. And there are probably another 300 on the way.
Of course there aren't. The best way is to know exactly the causal process generating the data, thus enabling you to predict your variable-of-interest perfectly from the limited data.
This isn't currently possible, of course, but there's one best way to interpret data, not an infinity.
For your tortured analogy to bear any fruit you have to assume that we're just waiting to find a dead simple set of equations that explains all human endeavor, and after that finite memory and computation power won't be an issue because, hey, cause and effect.
As far as I'm aware doing probability and statistics correctly is all about framing the problem correctly. You can, for example, accidentally assume that two events are independent when they're really dependent. Is it even possible to detect that kind of mis-framing automatically?
That's part of what I was getting at.
But the other was that "Webmaster" was a really hot ticket for a few years. It's faded markedly.
I can't see my parent comment at the moment, but I saw a recent "ask HN" post where the question concerned where all the Ruby-on-Rails jobs had gone. What had been a really hot ticked for the past few years is starting to fade. A lot of kids are starting to learn their first talent-shift lessons.
If you're using it in the big data, industry buzzword sense, then I doubt the OP would consider it "hard science research."
It's starting to read like "webmaster" salaries in the late 90s...