Sure, I've been doing that "intuitions about equations" thing since 1993 (my undergrad thesis was on using gradient descent to train the weights of a dynamic programming algorithm that found e.coli gene). I generally agree, to be a top ML researcher, you need those skills in excess of the average (I worked with quite a few of those people at Google). To do state of the art work? Mostly hard work, lucky guesses, lots of compute power, and a huge support apparatus to make rapid experimentation easier.
But the vast majority of people working in ML don't need that. Sadly, most of the work I did for one of the world's most powerful machine learning systems was literally computing frequencies and then sorting by the frequency, so features that were more common were encoded in smaller varints, saving lots of disk space.
But the vast majority of people working in ML don't need that. Sadly, most of the work I did for one of the world's most powerful machine learning systems was literally computing frequencies and then sorting by the frequency, so features that were more common were encoded in smaller varints, saving lots of disk space.