I agree that the median actuary would do poorly in data science, mostly because they don't have the programming ability for it. But the median actuary isn't trying to be a data scientist, so that's not who you care about if you're hiring for a data science position.
In most cases it’s been people who couldn’t pass exams, or were missold on what actuarial work actually involves and ended up moving early in their career when they wouldn’t have strong business acumen at all.
The one person who made the switch who I would consider a good actuary went back and did a masters in data science and applied for entry level roles. He had a couple million in the bank and decided he could afford to start over again.
He’s doing well now, he’s in a relatively senior position at a tech company. Financially he’s worse off than he would have been if he continued down the actuarial path, but he enjoys data science work a lot more than his old job and isn’t limited to insurance/pensions/finance.
I agree, I just think it's statistically a net negative signal if conditioned only on years of experience. I would expect a generic technical BS or MS in a DS-relevant field to be more qualified than someone who passed actuarial exams, if the two are at a similar point in their careers.
That said, competent employers shouldn't be relying on unconditional signals, they should be interviewing and testing and getting more information on the candidate. For such employers, the signaling value of the actuarial credential ought to be neutral.