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:
- Companies want candidates with very deep rather than broad expertize, are not willing to accept telecommuting, and will not train a new employee, and in some cases only hire Ivy league candidates. In doing so, they drastically restrict the pool of of potential employees
- Many university curricula are outdated, so despite the volume of applicants, hiring managers complain that very few have the right skill set. Few candidates are willing to acquire these new skills (e.g. Mapreduce), although it can be learned at no cost if you have an Internet connection and a browser.
- Data Scientists are not properly used or hired. In the case of Facebook for instance, one might ask how - despite all the great scientists and great data that they collect about users - they generate so little revenue per page impression. They should generate 10 times more revenue if data science was fully and properly leveraged by top management, read comments here for details. In this case, the issue is probably poor communication between top management and data scientists: a solution to significantly increase ad revenue by optimizing ad relevancy is described in my free eBook, so there is no excuse for poor performance. The same can be said about many companies (Google under-utilizing its Internet real estate, Microsoft having very poor marketing campaigns and not hiring the right people) and many problems such as spam detection or fraud detection.
- There are many alternate options outside traditional employment for data scientists, and as a data scientist, you should consider these new options.
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.