My undergrad/grad work is in Physics; I presently consult on statistics and AI (and other areas); I may soon start a part-time PhD on how to explain AI models. I am presently, as I type, avoiding rewriting a system to explain AI models because I dislike doing things ive done.

Its quite hard to see the full picture of how these statistical models work without experience across a hard science, stats and AI itself. However, people with backgrounds in mathematical finance would also have enough context. But its seemingly rare in physics, csci, stats, ai, etc. fields alone.

I'd hope that most practitioners in applied statistics could separate properties of the data generating process from properties of its measures; but that hope is fading the more direct experience I have of the field of statistics. I had thought that, at least within the field, you wouldn't have the sort of pseudoscientific thinking that goes along with associative modelling. I think mathematical finance is probably the only area where you can reliably get an end-to-end picture on reality-to-stats models.