Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician.
"I can write better than anybody who can write faster, and I can write faster than anybody who can write better."
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Leading to a generalized strategy for success:
1) Identify two orthogonal metrics by which your work will be judged by relevant stakeholders.
2) Work / practice until you are better than anyone by at least one of those metrics (in practice, better than the large majority of your competitors).
The degenerate case involves being the best in the world by at least one of the metrics; more achievable is to be somewhere in the middle for each. To use our own patio11 as an example: be better at coding than anyone who is better at SEO, and be better at SEO than anyone who is a better coder.
2) Out of the set of people who are at least as good at A, you are better at B; and/or of the set of people who are at least as good at B, you're better at A.
or
2) Nobody in the world is better than you at both A and B simultaneously.
I don't typically sell engagements primarily for SEO, by the way. (I also don't think that it is the case that I would not be strictly dominated by someone, somewhere on those two axes. If you phrase it like "Among the universe of people a potential client could actually hire" then my confidence that there exists a better option than me falls rapidly for each constraint you impose on the hiring pool.)
(But seriously: I do sell myself as the technical guy who understands business, and it works wonders).
Data scientists are to business analysts as Sanitation Engineers are to janitors. And that's all. Everything they think they're discovering, guys already did with supermarket loyalty programmes, frequent flyer programmes, etc 20 or 30 years ago.
Read and learn: http://en.wikipedia.org/wiki/AAdvantage#History
Wouldn't we call what @cloudera refers to, "data engineers"?