This is a deeply misleading (though somewhat accurate) comment. The reason it's misleading is because the 70% above (who may be called data scientists) are not

This is a deeply misleading (though somewhat accurate) comment. The reason it's misleading is because the 70% above (who may be called data scientists) are not actually data scientists, at best they are data analysts. In general, the core difference between data scientists and data analysts is that the former can code in at least one language (SQL doesn't count, unfortunately). However, because the term data science became so popular, everyone re-branded their analyst roles as data scientists leading to this concern. Additionally, the post I'm replying to is pretty biased, as the OP talks about productionising models. While this is a major facet of DS work, it's not the whole thing. TBH, I can find people to productionise models a lot quicker than I can find people who can figure out what to model, and how to measure it. Some of those people are most comfortable with Excel, and while I'd prefer they used a different tool, I can't argue with their output. Also, the OP here is focused on deployment of Python ML models, which again is a subset of a very, very broad field. That being said, i agree with most of the categorisations, except that the two critical attributes of good data scientists are a strong background in statistics and data common sense. Data common sense is a weird attribute where when you look at the numbers and see if they are reasonable. For example, if you are running a mobile gaming company and see an ARPU of $5, something has either gone horribly wrong, or you're going to be a billionaire (assuming you have equity). This attribute is actually not that common amongst DS people, so it tends to be the limiting factor, rather than ability with containers and deployment (which I do agree is very important).