Data science as a field (and as a job title) is broad and generally splits into
- research
- software engineering and data work
- product/business decision making
With many data science jobs being some blend of the three instead of staying purely in one category. And you can split each category even further. The point is that there is so much breadth that people can have the same title and completely different job descriptions. You need a disclaimer about this pretty much every time you discuss anything data-science related.
The article focuses exclusively on the third category of data science (as does most of the criticism of data science). So to understand the article as criticism towards the field, you need to take two things into account
- what proportion of all data science jobs are predominantly in the third category or involve any influence on business/product decisions (anectodally, not that many)
- how many of those jobs and teams actually exhibit the dysfunctions listed in the article
I don't have any numbers to answer these questions.
Personally, I'm interested in the engineering type of data science jobs (ML engineering, scientific computing and other adjacent roles). I wouldn't want to be in a position where I have to advise on decisions, or come up with justifications for someone's business/product choices, or continually have to come up with excuses for my existence in the company. In my current job search I have a preference for positions titled "software engineer" just to avoid all of this ambiguity. It's also very tempting to switch to being a backend developer or something similar and drop the whole data science thing altogether.