Also, not sure these archetypes do a great job of capturing the process-oriented/results-oriented divide that shows up in the opening anecdote (and which I agree is quite real).
Also, not sure these archetypes do a great job of capturing the process-oriented/results-oriented divide that shows up in the opening anecdote (and which I agree is quite real).
From my perspective, there is an impedance mismatch between the triplebyte process (heads-down algorithm nerd) and the findings that they are presenting here (product-focused, culturally well rounded hacker, been around the block a few times and has experience in other startups).
This doesn't describe academic programmers. In my experience, academic programmers tend to be glorified essay writers who can just barely produce parseable syntax if left alone for a few hours.
Having not got into programming by pursuing CS in university and instead kind of taking the old "hacker" route, that kind of thing always hangs around my neck when job hunting.
I'm on a team with somebody who studied CS at the same university where I studied English for a time, and just the same we tend to have a reciprocal knowledge share occuring just the same.
It's getting that initial in where the degree seems to cover a great divide.
A cursory glance of glassdoor suggests this as well - balance a binary tree, huffman encoding, bloom filters etc.
https://www.glassdoor.com/Interview/Triplebyte-Interview-Que...
We've helped many engineers who didn't perform well in the algorithm/data structure section still find jobs at great companies because we work with many companyies who don't care about those skills (as evidenced by not evaluating it during their interview). This is one of the main ways we can help engineers have a better job search process, if textbook CS knowledge is not your strength we won't match you with companies who evaluate that during their interview.
Pretty sure that's what Triplebyte's academic archetype is getting at. Also pointing in broadly the same direction:
http://yosefk.com/blog/why-bad-scientific-code-beats-code-fo...