(only half joking)
Software companies are satisfied with the job they've done at commoditizing programming talent but, at least for now, having a half-decent grasp of any specialty (e.g. machine learning, information retrieval) requiring mathematical firepower puts one solidly into Type-1 employment, which is where one wants to be.
"Data scientist" seems to be a way of saying, "yes, I code but I also know math, so use me for Type-1 work only".
You make it sound like a bad thing? Despite the rah-rah I hear from programmers about how they are unique snowflakes, being only a programmer is like being a janitor. A prime way to get discarded at the age of 40. If I can make sure that I am valuable because I bring other things to the table (Math, Product vision, people skills), why on earth wouldn't I rebrand myself to better reflect that?
There are plenty of "data scientists" who use canned tools and play around with parameters because that's all "the business" thinks it needs.
You want to trim complexity for a reason that any data scientist worth his salt (and there are plenty of celebrity engineers in SF making $500k who aren't worth their salt and don't know this) should already know: bias-variance tradeoff (see also: underfitting and overfitting). If your model is too flexible/complex, it will begin absorbing noise. That leads to a model that performs extremely well on training data but fails miserably on unseen data. There are well-studied techniques for preventing this, but I'd guess that fewer than 20% of self-described or titled "data scientists" are familiar with them.
As with a software engineer, it is a role that is different in every place. Every place has its own definition of the role. This is not bad. It is a mere reflection of the market conditions where there are a lot of people are simultaneously bad at Linear Algebra, Probability and Statistics and dangerous enough to write production code fast. (Your standard C.S. grad SWE).
