Some others have commented that this isn’t “required” but I can tell you that solid understanding of Bayesian statistics makes everything make way more sense and gives you a great basis for building more understanding of any ML algorithm. I don’t mean just memorizing Bayes’ theorem and answering the canned “99% accurate test is positive for a disease that affects 0.01% of the population” interview questions, but understanding relationships between methods (GP’s, SVM, neural nets, tree based things) and how they relate back to Bayesian statistics either directly or by approximation is key to having true visibility into what you’re doing. Sure you know about loss functions and regularization, but if you can’t look at a loss function and translate it into a precise statistical statement in terms of priors and likelihoods, you don’t truly know what’s going on. You also will have a hard time extending other loss functions/regularization schemes in a way that isn’t problematic, and explaining/justifying the choices you make on that end. Thinking in terms of Bayesian statistics (Bayesian stats IS stats, it’s mathematically equivalent to frequentist statistics but has a different philosophical interpretation) saves you from many many rabbit holes that in hindsight are obviously Bad Ideas when seen from a more Bayesian point of view. Not to undermine people without a strong statistical background, you can still do good work, but your life becomes easier and less ad hoc (in my experience) once you understand things statistically. You should ideally understand the assumptions behind your models, and what the implications of those assumptions are, and it’s hard to do that consistently and rigorously without Bayesian statistics.

That being said, Bayesian statistics is not SUFFICIENT for doing data science well. You need to know pragmatic solutions to intractable problems, what heuristics might be ok to try, how to EXPLAIN things to people, how to code well enough, etc. But boy does knowing Bayesian statistics help.