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> "I think data scientist is a sexed-up term for a statistician." As a statistician-and-engineer who is currently on the job market (my graduate program finish

> "I think data scientist is a sexed-up term for a statistician." As a statistician-and-engineer who is currently on the job market (my graduate program finishes this spring), I feel this pain. I've been referred to as a "data scientist" multiple times (that's even been my official title at work before), though I do still cringe sometimes when I hear the word, for this exact reason. That said, I don't usually present myself as a statistician, even though my degree is a statistics degree. Most people who hold statistics degrees are fairly lousy engineers[0], and I don't know of any other term that (concisely) expresses that I'm equally competent as a statistician and a (backend) engineer[1]. Of course, this is because many of these programs haven't yet caught up to the fact that computers exist and are still teaching statistics as if we're in a pre-computation era. The perfect solution is to fix this, and thereby fix the connotation of the word "statistician". It's the same reason I dislike the term "growth hacker" - really, that's just the way marketing should be done (ie, based on numbers and verifiable statistics). In a perfect world, all (competent) marketers would be "growth hackers". But many marketers aren't, and so we have to make up another cringe-worthy term for it. Unfortunately, that's a problem that's beyond my means to solve. So I bite my tongue and add the word "data scientist" to my resume anyway. [0] Usually self-proclaimed, too [1] ie, "I could work as a backend engineer if I wanted to/needed to, but I'm looking for work involving both skillsets"

textminer 13y ago
I've actually seen "data scientist" used to indicate "probably not that proficient as an actual engineer, but knows what he or she is talking about in terms of theory and can usually get a working model done in R, Matlab, or Python". There seems to be an additional jump in knowledge of systems, data structures, and algorithms in building up performant machine learning stacks (something which can add in practical knowledge and praxis, but steals a little from raw-ideas work). Citation: working in data science and machine learning engineer roles for nearly three years straight out of grad school in math.

textminer 13y ago

I've actually seen "data scientist" used to indicate "probably not that proficient as an actual engineer, but knows what he or she is talking about in terms of theory and can usually get a working model done in R, Matlab, or Python". There seems to be an additional jump in knowledge of systems, data structures, and algorithms in building up performant machine learning stacks (something which can add in practical knowledge and praxis, but steals a little from raw-ideas work). Citation: working in data science and machine learning engineer roles for nearly three years straight out of grad school in math.