Do you think it's normal for data science projects to have some amount of "what should we do" -time? Or does it mitigate by experience?
This is really about differences in the expectations of your employers and you.

In general every job depends on continued learning and making time to expand your knowledge. Data science as a very heavily knowledge and methodological based occupation and therefore life-long learning is even more of a thing.

But how and when to do that learning? In my experience most employers expect you to do that sort of learning on the side or with irregular, slotted and scheduled training.

However I think it is quite normal that a big part of learning is during projects / on-the-job like you describe. Experimenting with new methods, reading up on other solutions, etc.

At the end you have to balance these expectations and just make sure, that your "research time" is goal-oriented and efficient. Don't spend hours reading up on "interesting untested solutions" but also don't worry if you spend time on datascience.SE to look up solutions and guidance.