There may be a lot of uncerntainty in Data Science and ML projects. However, recently I started feeling like I actually have it better than someone from pure software engineering sides of things:
For either, there is often a function from time spent to quality. 100% perfection is basically impossible and before that the function increases very slowly, seemlingly logarithmicly.
For SWE, expectations are often close perfect solutions. Too greedy effort estimations cause a lot of trouble. For DS/ML, however, perfect is usually off the table and this fact is widely (not universally though) accepted. When it is accepted to give estimates in this way, suddenly there no harm from being quoted on it and I really don't mind to give estimates anymore, where I just make a guess at a good 80/20 point. If I am wrong with that point, chances are nobody on the outside/higher up ever knows.
This may be different in domains where very clear targets have to be met (e.g., "self driving cars that pass lawmaker's requirements for use on the streets") and then I'd guess it is a true nightmare.
Like this, I never felt overly pressured by ML/DS deadlines over the last years. Some things were great successes, sometimes the quality wasn't great enough and projects were stopped or customers left. But there never really was a case where anyone thought that working extra long might have been an option to meet higher expectations.
I don't really have a solution for SWE, I don't really see how one would sell something like "I can do it in X time and it will only crash / stop working / make mistakes / become too slow / have vulnerabilities so often. More time will lead to fewer problems". This just isn't what's expected. But at least for complex systems and security vulnerabilities, I'd argue it is actually quite true. Guarantees for 100% perfection just aren't realistic. Avoiding the most obvious pitfalls is done rather quickly and the more time spend, the more is needed for further improvements.