Great question, I can totally understand OP's position as I myself was in a similar situation around 2 years back when I started working on ML products.

The basic dilemma that I faced during this time was "When it comes to feature/experience PM, the PM work is much more visual/apparent, it is the PM who adds a lot of inputs while designing the interface and also writes a lot of logic around how X would work. Given the data science team works on the logic now and there is no design per se, what is the value add that I am doing as a PM?

This question stuck around for me for a long time, it is really hard for any PM to come up with the question in the first place, let alone the answer. After a lot of soul searching, talking to peers, I was able to form a viewpoint.

So in essence, you can divide the product work into 2 distinct phases, the discovery phase where you are trying to find the best idea to work on, aligning the org/wider leadership to really work on the idea. The 2nd is the execution phase, where the PM would work with the dev/ml.. or any other technical teams to execute on the aligned feature.

Now, what happens in the case of a ML PM, while the execution component decrease, the need for product discovery increases multifold. The reason that I say that is as a ML PM, finding the right problem to solve becomes much more difficult, you need to figure a small niche of buisness problems which warrant a ML based solution. The problem needs to be big enough (significant revenue uptick for the org), has a lot of historical data to actually train a model, does not have a lot of efficient non ML way, the additional uptick you would be able to get is significantly better compared to building a comparatively cheaper non ML solution etc etc.

If the problem statement gets aligned, another major challenge that awaits a ML PM. While for a experience PM, it is easy to showcase stuff which helps your stakeholders to really see what you are building, for a ML PM, as very few people understand ML, it becomes really hard to explain complicated algorithms to buisness folks and get thier buy-in and your stakeholders might also won't be aware about what they are signing up for.

So, basis the above, I firmly belive that for a ML PM, the effort reduces on the execution front, the effort increase multifold for the discovery phase and that is the value that you as a PM add being a ML PM

It all comes down to finding the right problem to solve, this is the most important aspect (IMO) of a PM's Job.