This is sadly misguided because it mistakes the behavior that’s good for the company (fast iteration loops, tight alignment between product and engineering) for the means to get there (make data scientists be more end to end).

The goal of tight iteration with good alignment is of course a good one, but verbal sleight of hand doesn’t mean the way to achieve it is with more end to end responsibilities for data science.

The huge cost of course is that data scientists and ML engineers have a hugely asymmetric comparative advantage when spending their time on model training and statistical solutions. You want them at full utilization for this set of tasks because nobody else you employ can do that same statistical work, and that work is often hugely valuable whereas most of the end to end work is frankly grunt work and fighting through errors that anyone can do.

If you hire Michael Jordan for your basketball team, why would you make him spend his (expensive) time cleaning up soda bottles or checking the elevators for maintenance issues? It utterly makes no sense and wastes the comparative advantage - all your Michael Jordans will be heading for the door.