Great post. A few random comments...
> The median data scientist is horrible at coding and engineering in general. The few who are remotely decent at coding are often not good at engineering in the sense that they tend to over-engineer solutions, have a sense of self-grandeur, and want to waste time building their own platform stuff (folks, do not do this).
> It was obvious that there is a general industry-wide need for people who are good at both data science and coding to oversee firms’ data science practices in a technical capacity.
The job of overseeing a crowd of stubborn self-important over-engineerers sounds pretty thankless.
> 23 year-old data scientists should probably not work in start-ups, frankly; they should be working at companies that have actual capacity to on-board and delegate work to data folks fresh out of college. So many careers are being ruined before they’ve even started because data science kids went straight from undergrad to being the third data science hire at a series C company where the first two hires either provide no mentorship, or provide shitty mentorship because they too started their careers in the same way.
Startups are a low-paid job with a lottery ticket for a little dash of excitement. You get what you pay for.
> ...I live in constant anxiety that someone will pop quiz me with questions like “what is the formula for an F-statistic,” and that by failing to get it right I will vanish in a puff of smoke. So my brain tells me that I must always refresh myself on the basics.
Focusing on the basics is better than pretending to understand fancy things, but even this level of "continuous professional training" or whatever you want to call it is, to me, a bit off the mark. We can look up formulas whenever we want these days. We need more meaningful ways to test our understanding of things.