At our company if it's in a notebook it's not considered ready for production, it must run as a script before being considered for Eng to take over from DS. It's actually not that hard to write a notebook in such a way that it converts easily to a script. Just check and make sure that your variables/functions/whatever are initialized above the cell(s) they're used in, declare all imports in the top cell, and periodically move cells to fix any inconsistencies with these rules (checking that you didn't break anything of course). I've always said that Data Scientist doesn't mean, "I don't do engineering," good basic eng practice helps make more productive data science and brings it into production more robustly. How do you know your models work well if the code that generated them is inscrutable?

I wonder how much of the "3 engineers for 1 data scientist" ratio I hear all the time is due to Data Engineering being assigned the role of cleanup to code that should be better in the first place.