I interview a lot of developers for ML positions at our company. The first red flag is always a lack of math. Candidates who come in with API-level competence ie. can implement an ML algo using this,that or the other API, without any understanding of some basic math behind it, always fare poorly. Atleast in ML, not having an understanding of math is pretty much like claiming expertise in riding a bicycle by watching 100s of bicycle videos on youtube without ever riding a bicycle yourself. This isn&#x27;t the case with say, front-end web dev, where nobody really cares whether what&#x27;s under the hood is php or jquery or elegantly handcrafted elm as long as the webpage looks good and functions as advertised. You can get a lot of mileage by hiring someone who can &quot;make webpages&quot; without knowing what tools they use to make that webpage. Once you get traction etc. you&#x27;ll probably rewrite all of the cruft :) With ML, if you hire somebody who can &quot;create decision tree in python mllib&quot; without knowing the first thing about what entropy is &amp; how to compute entropy ( a real candidate, unfortunately ), you are simply inflicting a lot of pain on yourself and your customers. Suppose such a person is deciding whether or not to give you a loan, and he decides to construct a decision tree. He&#x27;ll happily throw in your zip code and credit card number into the mix, not realizing that those two features have super high entropy but the tree will have serious overfitting issues ie. the tree will simply not generalize to unseen data. He won&#x27;t realize these things because he won&#x27;t know what entropy is in the first place, since he only thinks of decision trees in terms of some black box that comes out of some ML api.