He might be trying to say that there are enough people like him in the market that it really shouldn't be a seller's market; but those others are mostly undervalued/ignored, while his value has been artificially inflated because of the (incorrectly!) perceived rarity of his skills.
This is the pattern that goes on all the time inside companies: constant attempts to hire talent from the outside, coupled with complete ignorance of the skills (perhaps developed post-hire) of their existing employees. I know more people than I can count who know everything required to be doing "data science" for a company, but—since they were hired to be maintenance dev-ops people—they will never be considered for "promotion into" a data-science role when there's a vacancy.
2) show me some code you have written and deployed
3) here's Learn Python the Hard Way, start reading and come back with questions.
In the modern era, I have not found anyone who can pass test 1 and doesn't know code (except for 1 mathematician who's like 70 and no one cares if he can write code, his ideas are so insanely good that other people write his code for him, despite their day jobs). These people execute and come back with challenging problems for me.
Anyone who passes test 2 seems to be able to pick up whatever task I give them. I'm not entirely sure they understand what they're doing all the way down, so there's more review, but they can generally execute. Questions go back and forth.
If I get to test 3, well, no one has come back with a question yet.
What this tells me is there is plenty of signal in the culture that math and programming ability are valued. The people don't, ain't never gunna.
If you know linear algebra, machine learning is not much further to go. You can understand pretty much all the models by looking under the hood at the math.
At that point it’s just experience to learn the rules of thumb that guide practical implementations.
Haha. I guess if I had to ask any question about python, it would be, "why on earth did they do that ridiculous unicode thing in version 3?" Most builtins should use bytes, and the few that can't should just use bytes annotated with an encoding.
Most builtins can't use bytes instead of string safely for all data, and the problem is that it propagates - in Python 2 you often have the case that library A uses library B that uses a builtin that treats a piece of text like a string of bytes, and so you can't use library A because it will give you broken results in certain conditions. We've spent time updating some third party open source libraries to support python 3, and that was well worth the time to avoid the waste of programmer time that'd come from working in python 2 due to lack of sane handling of unicode strings; and if you're working with user-facing software (as opposed to, say, scripts for system administration or physics calculations), pretty much every string you encounter nowadays is unicode string. Names of people, names of files, contents of files, results of http requests, results of database requests - all of those can be treated as streams of bytes only if you treat them as a single atomic token and don't look inside that stream in any way whatsoever. As soon as you make the first index operation, substring or split, you can't treat them as bytes anymore safely.
However, you can simply think of it as syntactic sugar that manages the encoding annotations in the default case. Where is it creating problems for you? Is it some performance hit or something else?
I think there are a lot of deep learning dilettentes, and a large number of them on HN, overestimating their abilities relative to the experts being hired by google. The guy who is stuck doing dev ops but can be doing data science is not the 1mm+ guy at google even though some of them find it hard to accept
True, but they probably are worth more to their own companies—especially mid-sized companies—than those companies realize. Whether or not the market would pay more for them, more money could certainly be being made off their backs (as with the “administrative assistant” roles at medium-sized companies that often end up substituting for much of the role of HR, PR copywriting, office management and often even team coordination. All for the cost of a secretary!+)
Not harnessing these untapped assets you already possess is a failure for a company, in much the same way that not shaving cost centers or negotiating purchases would be.
+ Yes, I’m trying to hint here that one would be crazy to allow oneself to be exploited in this manner. If you truly have the ability to do these additional jobs, then you should be applying for roles that explicitly, rather than implicitly, use those skills, and offer compensation for them. Sadly—for the same reason many people find it hard to negotiate salary—many people won’t try for jobs that no one has told them they’re “allowed” to apply for.
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.
>Otherwise they might as well not know how to do it.
This is some real crazy talk here. The idea that you should limit yourself to the particular set of knowledge you wish to make money off of is insane.
There's almost no such thing as wasted learning, even if you're not interested in pursuing it for a career. Maybe it's a hobby, maybe you touch it in a tangential way for your normal work where a basic understanding brings value but is not necessary.
This is the same attitude that undervalues previous experience and builds in favor of specific lingual or stack competence.
>> Otherwise they might as well not know how to do it.
> This is some real crazy talk here. The idea that you should limit yourself to the particular set of knowledge you wish to make money off of is insane.
Crazy talk? That's what you get when you blatantly ignore half of what I said. In the previous sentence I quite literally asked if these want to be doing data science to begin with. If they don't have any interest in it, then it doesn't matter whether or not they know how to do it as far as the company is concerned; they're not going to be doing it either way. Nowhere did I ever suggest that it's somehow a good idea for people to be limiting themselves to a particular knowledge set.
This is the pattern that goes on all the time inside companies: constant attempts to hire talent from the outside, coupled with complete ignorance of the skills (perhaps developed post-hire) of their existing employees. I know more people than I can count who know everything required to be doing "data science" for a company, but—since they were hired to be maintenance dev-ops people—they will never be considered for "promotion into" a data-science role when there's a vacancy.