So assuming this is true, what plan of action do you recommend for an “old gen” data scientist who is strong in math, stats, ML theory, R, dataviz, ETL, research, etc., but who is not by a long shot a “full fledged software engineer”? I will soon be competing against this new crop of statistician/engineer superhybrids you speak of.

I know a fair bit of Python (mostly for ML/DL applications), bash, and just a smidge of HTML/CSS/JS (just enough tweak a front end demo via R Shiny). I’m OCD enough that I make every effort to write clean and reproducible code and unit test it as I go (is this TDD?). I can implement some stats algorithms (e.g. EM algorithm, MCMC) from scratch with a pseudocode reference, but I rarely if ever have the occasion to do that for obvious reasons. I understand the concept of computational complexity, though I don’t have any figures memorized.

But I’ve never taken any CS course beyond Programming 101. I wouldn’t know how to navigate a complex production codebase. Embarrassingly, I know almost nothing about git. I’m 100% sure I’d get slaughtered in a whiteboard interview or similar. For that matter, I could easily get nailed on some holes in my core data science knowledge (cough SQL cough).

So, do I rebuild my foundation around software engineering, or just patch up the obvious holes? Grind towards a management position and let my inferior skills rot away?