People will agree, in theory. But here's what happens. The data science team will grill the generalist about finding a steepest descent vector and how logistic

People will agree, in theory. But here's what happens. The data science team will grill the generalist about finding a steepest descent vector and how logistic regression works, and conclude that the candidate just isn't going to be populate, run, and interpret output form a neural net. Then, the data engineering team will ask the generalist to write code to print the path from one leaf node to another in a binary tree at the whiteboard in 45 minutes, and conclude that the candidate just isn't going to be able to figure out how to identify matching terms in different parts of a JSON tree. Eventually, both groups withdraw into their own silos and confuse each other. They may decry the lack of generalists, but when it comes time to hire, they will resolutely not hire candidtes with 80%ile skills in both areas. They will hire only people with 95%ile skills in one area. They may get some people who have well rounded skill sets through sheer chance, but their process selects against this outcome.