Thus many "data scientists" are juiced-up report-builders who can't analyze their way out of a paper bag.
In my experience, this is true:
> "data scientists" are expected to be the equivalent of full-stack engineers (or maybe more accurately: one-man CTO shops)—to understand data architecture, understand business architecture, ensure data quality, build data into product, build dashboards, derive insights, posit hypotheses, set strategy, and drive business value.
But this is not:
> Thus many "data scientists" are juiced-up report-builders who can't analyze their way out of a paper bag.
Rather, the data scientists are trained in only two of the requirements you mentioned: derive insights, posit hypotheses. The rest is all self-study and on-the-job experience. This means that we are putting unrealistic expectations on data scientist and/or their training is insufficient, not that data scientists are somehow morons.
Indeed, my context here is that people who wear the data scientist title come from multiple backgrounds, and are often asked to wear too many hats. They are non morons—they may be darn good report-builders, but haven't been trained in insights, for instance.
If you're reacting to my word choice in that last sentence, know that I am frustrated with people who claim to be data scientists but can't derive insight. (And we can argue about "many".) But that's not a broad denouncement against all data scientists, either.