You’re right, but in ML/DS (and I would argue, even in other contexts you might not consider ML/DS) you usually are doing things in a Bayesian context (“what can I say about my model parameters given my observations and assumptions”). If you’re only doing OLS with maybe a L1 or L2 regularization, it’s not critical you understand the Bayesian interpretation, but it helps, and as soon as you start venturing out into other ideas and messier/biased data and want to modify something, you’ll need a Bayesian understanding of what you’re doing.