Basically the more full stack you can be the better. In a perfect world you have have the ability to frame statistical problems, decide on a modelling approach

Basically the more full stack you can be the better. In a perfect world you have have the ability to frame statistical problems, decide on a modelling approach and evaluate it, deliver it not as a notebook but as a Python library, build the data pipelines to integrate it into the business, write all this code as an experienced software engineer would, then be able to visualise or reason about the model outputs with an eye to the wider business. In reality, few people are good at all these things, especially early in their career, but they’re all worthwhile skills to pursue. In terms of prioritisation, Python and general software engineering skills multiply your ability to deliver stuff but also learn and experiment with new libraries. If all you knew was logistic regression or gradient boosting, but you could deliver a whole pipeline and insights on top of it, you’d be extremely valuable to most businesses. You’re also set to fill those roles in future startups you found yourself, where needed. It’s true you can also do world class research and never leave Jupyter - that’s valid too, but probably more hit and miss as a career.