The running assumption on HN seems to be that ML/data science jobs are easy because you can just "plug in" already implemented algorithms and just expect things to work. I think the barrier for most of these jobs is a proper statistics education--one that involves active exploration of real world data that's imperfect and has to be cleaned. Very rarely is there an obvious solution to big data problems and much of the work is tedious parameter tuning that absolutely does require knowledge of the math and domain and not just how to write k-Means.