[jeeeb]

Good list. One thing I'd add, which you kind of hint at:

Good practices from software engineering are just as applicable to Data Science. In particular:

Notebooks are great for performing an EDA, and testing out new concepts. They're not great for running production code. Put your non-once off code in regular source code files and source control it.

Break your code into separately testable and composable functions. Write unit tests to verify behavior where you can. Speaking from experience you all most certainly will find bugs.

Implement a peer review process for the methodology used and the code. Approaches should be explainable and justifiable. Bugs and poor assumptions can lead to incorrect results.

Focus on making your model training process end-to-end reproducible. Document the training data used. Document the configuration used. Link back to the commit hash of the exact code used. Make sure your environment is reproducible.