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I think that most companies do not want to take risks and do deep learning. They are sold big data platforms and cloud data platforms to process structured data and do analytics, so they need random forests and xgboost, they don't want to take risks with deep learning.
Way too many candidates with PhD that are not really good either for industry. They are hyper focused on the area of their PhD's thesis and don't know much outside of it. (Compared to somebody with a master's and experience, or just a lot of experience).
After all, what are the odds in industry that you are working on a project that is directly relevant to the hyper focused specialization of the PhD? I recruited 5 scientists over the years, with 2 having a PhD, and the 3 others not. The 2 PhD while brilliant on the theoretical side, could not make reproductible experiments due to poor coding capacity, and couldn't explain clearly and simply anything to non scientists stakeholders. They overall underperformed compared to the non PhD. I even had to fire one after a Pip (Not Amazon).
Of course it could just have been selection bias, or just a coincidence with just 2. But 2 out of 2 underperformed while 3 our of 3 performed adequately, or overperformed.