The academic math types with no ML experience don't fare much better than the no-math practitioners with experience from what I've seen. They generally have terrible mental models of how the math behaves when applied to real data.

IME the competent data scientist needs to have a high-level understanding of the concepts underlying ML (stats, optimization, central limit theorem) and to be comfortable reading math to understand the low-level details when they are relevant. In addition to actually understanding the ML techniques they're using obviously.

On top of that, I would rather work with someone who can code well than a pure mathematician with a ton of experience proving theorems (and no coding ability). Outside of a few industry research jobs, the cost:benefit ratio of making substantial theoretical breakthroughs in most everyday ML work doesn't justify a lot of time doing theoretical proofs.