Contrast this post with those you see with ML hobbyists who delve into medicine or fake-news and produce useless results testament to their lack of domain-specific competency.
The successful application of ML requires a deep understanding of the domain it's being applied in.
(Yes, I know they do much more than ML, but still)
The historical record overlooks the people he hired who knew a thing or two about trading, while fixating on the team of NLP scientists he hired from IBM. Likewise Simons wasn't initially successful in the very, very early years. It wasn't until the late 80s that the Medallion firm really came into its own.
Funnily enough I think the ML hobbyist problem is most pervasive in the "predict the stock market" domain. There was a post on HN a few days ago [1] that was overfitting the validation set and hand-waving away fees and spreads. The author concluded that "there was no subtle underlying pattern" because they failed to find one.