My impression is that the article mostly describes data analysts, not data scientists, though of course job titles are fuzzy and variable. IME data scientists p

My impression is that the article mostly describes data analysts, not data scientists, though of course job titles are fuzzy and variable. IME data scientists pretty much universally at least attempt to work on things that directly and demonstrably increase revenue or reduce expenses. That experience is colored by the fact that I work in a field (insurance) where statistical analysis is core to the product, but my impression is that it's true more generally - if you're a data scientist in e.g. conversion optimization at a big company you probably have no trouble justifying your salary, and even the relatively unsuccessful data science teams I'm familiar with tend to work on things like staffing level optimization that are at least plausibly a value-add for the business. The more interesting line of argument to support data science being a bullshit job is that all of these lucrative use cases are just a tragedy of the commons. Insurers in aggregate did just fine when pricing was based on actuaries using simple approaches to "square the triangle" and then adjust rates based on rules of thumb, the only reason they can't always do that today is that everyone else has data scientists. And if you don't have a good recommender system, your customer will just buy somewhere else (or "worse", avoid an unneeded impulse buy). Of course, it's not strictly zero-sum. Insurance pricing is "fairer" now, which is probably a good thing, and data science is enabling all sorts of obviously positive-sum products like wearable health monitoring and car safety features (to say nothing of self-driving eventually). Other use cases like "engagement" optimization are probably negative-sum. But in general my point is that creating millions of dollars' worth of business value per year, as many (most?) data scientists do, doesn't preclude you (or me) from having a bullshit job.