This is really a myth. Most ML jobs require very detailed understanding of statistics because the devil is in the details.
You need to understand things like multicollinearity, coding biases, missing data techniques, convergence of Markov chains, learning curves, mechanics of various higher order gradient optimization methods, how to really carefully evaluate goodness of fit in a huge range of categories of models (neural nets are the vast minority of all models used in production settings) and a ton more beyond this.
If you read “tensorflow for hackers” and believe you can write production neural nets, it’s a disaster.
I can just tell from my own experience, having interviewed and researched a ton of ML jobs: The majority of ML jobs today seem to be re-branded analytics jobs.
I'd say that a solid 4 in 5 of the jobs I've interviewed for, which were tagged in the ML domain, were just that. Typical [x] analytics jobs which don't really require more than stats 101, and good handling of excel. Basic scripting knowledge were often in the nice-to-know section.
Now, there might be a world difference in the typical ML jobs you see in startup hubs like SF, and the jobs you see elsewhere - but companies are, and have been for almost 10 years, been desperate to get onboard of the hype-train, and have re-banded a lot of jobs to attract those wanting to work with ML or Data Science.
You need to understand things like multicollinearity, coding biases, missing data techniques, convergence of Markov chains, learning curves, mechanics of various higher order gradient optimization methods, how to really carefully evaluate goodness of fit in a huge range of categories of models (neural nets are the vast minority of all models used in production settings) and a ton more beyond this.
If you read “tensorflow for hackers” and believe you can write production neural nets, it’s a disaster.
Garbage in equals garbage out.