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The screen shot below says it all: candidates for data science positions are plentiful, considerably more abundant than job openings: more than 600 LinkedIn users applied for this data science job. Even less prestigious companies routinely attract more than 50 applicants per job opening.
In view of this, you would thing that getting an expensive analytic degree is a waste of money (except a degree from MIT, Stanford, CMU, Northwestern and a few other select schools), and applying for a data science position is a waste of time (you would be competing with top candidates who apply to all the advertised positions). The problem is actually a bit more complicated. The root causes and solutions are as follows:
Finally, an unexpected consequence is a rise in sophisticated fraud, as an oversupply of unemployed math PhD's with great expertize end up working for rogue organizations (their only choice), while government and other organizations fail to hire the best people, for whatever reasons.
Comment
I can't always agree that managers aren't willing to listen. I believe more often than not, the problem comes from not being able to communicate your idea in a persuasive way. So many really smart people suck at this and then blame it on their managers or their customers.
That's why one of the most sought after skills is the person who can take complex ideas and math and break it down to a point that even a child could understand.
As much as smart guys hate salespeople, you have to accept the fact that you're a salesperson whenever you're presenting an idea. If you get good at selling, you'll notice that you will have less complaints about people not understanding.
@Mike: The test was performed according to design of experiment best practices. The test is one element among many others (e.g. number of applicants per data science position posted on LinkedIn, number of resumes that we receive each week, number of satisfied clients when we post a data science job ad on their behalf, number of applicants we receive when we advertise a position for ourselves) clearly indicating that for the average person, acquiring an analytic degree or applying for an analytic job in US is a waste of time, possibly worse than playing lottery in terms of expected (negative) return. Of course there are exceptions, e.g. if you have a CS master from Stanford acquired recently, and good experience with processing large data sets.
On the plus side, we propose very interesting and challenging alternatives to analytic candidates interested in leveraging their skills, alternatives that don't require sending resumes. Read this article and all the comments to learn about the alternatives - if you are a candidate. If you are a recruiter complaining about lack of analytic talent, talk to us - we'll quickly find great talent for your data science positions.
Vincent,
I don't think your test proves anything at all. There are a raft of biases. Not the least of which is the exact reverse of what you did. The posting of bogus jobs by recruiters looking for ammunition for their putty cannons. I personally have had no problem recruiting analytics capable individuals. I have experimented with a number of different styles of advertising, different sites, and different levels of role. You just have to look at the job postings that come into my email inbox where the same headline for generic industry roles with exactly the same text that has not changed for months which shows that recruitment companies think that spamming sites like this one are productive. They wouldn't have a clue about what works.
@John: Our test proves that the way many hiring managers recruit data scientists does not work. It proves that many great job applications go into black holes. It does not explain the reason why: it could be due to poor keyword filtering algorithms, incredibly long response time to an application, email not working (e.g. you contact an applicant via email but the recruiter's message goes into a junk box) or other reasons:
@John: We have strong data to back our claim, including hundreds of made-up, high quality, anonymous, varied, well targeted applications to more than 200 advertised positions (all requiring at least a BS in an analytic field), with abysmal response rate from hiring managers. This was part of an experiment to check whether the claim of lack of analytic talent was bogus or true.
Also, for any open position advertised on LinkedIn, anybody with a LinkedIn account can easily check the number of applicants who applied. Most data scientist / analytic positions (and you can check it yourself since this is public information) have more than 50 applicants. Additional anecdotal evidence (when we tried to hire analytic talent) shows that there is no shortage of highly qualified applicants (although we are a bit biased in the sense that we were accepting 100% telecommuting).
Any shortage would have to be in very specialized fields (positions requiring US citizenship + clearance), but quant is not one of them. Unless what you mean by quant is a cheap Java/C++ coder that in addition is a true data scientist. Indeed, these people don't exist and will never do.
It's completely amazing to me that in a world where these methods are applied so ubiquitously (and successfully) that there remains such a huge population, especially in management that have no idea of their existence. I guess everything has a bright side in that I rarely hear anyone connect "credit default swaps" or the flash crash in 2010 to quant methods ...
... we're on the same page Mike ...
I'm very careful even with simple concepts like Monte Carlo. A significant amount of what I do involves some form of MC .... I just rarely say it...
One of my exec supporters, meant as a term of endearment, even calls it "Voodoo" math! ;-)
Jwinburnc - it is even more distressing when a direct report to the CE does not understand the difference between a stock and a flow! We are having the same issue with understanding. We find we cannot use some better performing models because the business cannot intuitively grasp them. But hey, we have come a long way. Back in my early days Box Jenkins techniques we a black art and frowned upon, and data mining - the devil's spawn. If you could not justify the model within a range of currently accepted economic theory you got no traction at all. Nowadays at least we have some acceptance on the basis of ROI.
Even companies that get "it" at the senior executive level still struggle. As an example; Recently, my group produced a predictive model for a resourcing problem that when "back tested" proved an 80% improvement over current methods. When presented to a group of managers in finance, one of the managers responded "I don't understand the math, so I don't believe it." Fortunately others did "get it", and it was applied. This is our reality. ;-)
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