But yeah, where I've worked that's generally what we look for in candidates.
What is astonishing to me is how there seems to be 1) a dearth of candidates, period, and 2) candidates we can dig up miss scheduled calls, show up late for interviews, interview very poorly, turn in poor quality take home exercises (an exercise which essentially just covers the basics), have really crappy resumes (typos, horrible layout, inconsistencies with LinkedIn profile, etc...)--and these are folks with experience as statisticians or data scientists. Amazing.
We don't ask anything deep or complex either, yet we've had a really hard time finding people.
I think there's also an intersection with devops skills, maybe less important, but your hardcore statisticians usually put zero thought into operational considerations. Really the last bastion of "works on my machine" thinkers in the computing world. I just finished the Coursera "Reproducible Research" course and I was really struck how many of those principles parallel good software engineering practices - use source control, document through code, separate your environment from your code, automate as much as you possibly can, etc. I've been a software engineer for 20+ years but I want to get into data science partly because I've always been a data head, just without the theoretical background to do really interesting work, but also because I think I can bring some of the software engineering skills to bear.
Also, with grading peer's work on Coursera, I really realize that a lot of these candidates need help with their English and presentation skills. Many of the students put no work at all into the presentation, I imagine that's going to serve them poorly in the working world.
It seems to me that's an awful lot to fit between one pair of ears.
I would be interested to find out if you are using the phrase, and what would happen to your search for candidates if you changed the title to something less "sexy", like "data analyst"?