That’s correct, but the pragmatic reality of most companies and data science with respect to internal politics and other bullshit is that they’re not going to find that mobility even if they network internally
Funny -- I actually see that as the single most important factor distinguishing folks making $1MM and $100k. There are plenty of overpaid geniuses, but really even more underpaid geniuses.
I am not in agreement- while I’m sure there are a few, there’s quite likely more people who believe themselves to be underpaid geniuses.
The overpaid genius, at the very least, has a preponderance of evidence that he understood all the math he’s using and has evidence he can innovate with it rather than regurgitating code (maybe that’s slightly too lenient)
> The overpaid genius, at the very least, has a preponderance of evidence that he understood all the math he’s using and has evidence he can innovate with it rather than regurgitating code (maybe that’s slightly too lenient)
This assumes a separating equilibrium. There is one, of course, but it's biased against genius. Businesses don't want to overpay. Not should they. People don't negotiate their salaries. They should.
Not sure what you mean by separating equilibrium... bimodal? I think we’re talking about different things. I’m comparing (in response to the parent) the devops guy with a few dl/ml side projects who is possibly skilled enough to join a data scientist team and contribute vs. a high ranking stem phd with a few years of experience who the devops guy may be supporting. Both can certainly undernegotiate, but they’re in different situations/roles usually.
The difference between the two you mention could be qualitative, as you imply, or it could be that some of the DS folks send the wrong signal. Not choosing a top-tier AI university would be a poor signal, and fail to differentiate quality candidates of equal ability.
It comes down to whether the mental model of meritocracy is actually practiced by the business world, which also includes whether screening performed by employers is accurate. It's not, ergo it stands to reason there are poor AI geniuses too.
