It feels like you've determined there's nothing wrong with pyenv, pip, and virtualenv so any issues brought up, you will reject.
If that's not the case, here's the issue - someone used pyenv and did not exactly specify the python type - I believe we were on 3.9 and prod was 3.9.11 and the current python version was 3.9.12. There was a downstream package that had an OS dependency - I believe it was pandas - that conflicted and needed to be updated locally to work with 3.9.12. This broke and raised an error in production that was not reproducible locally - and when you deploy on AWS, reproducing can be a pain in the butt. I'm sure if the data scientist had used perfect pyenv, virtualenv, and pip commands; we would have caught this. However, they're very complicated - especially for people who focus on math - so requiring full knowledge of these tools is unrealistic for most data scientists.
If that's not the case, here's the issue - someone used pyenv and did not exactly specify the python type - I believe we were on 3.9 and prod was 3.9.11 and the current python version was 3.9.12. There was a downstream package that had an OS dependency - I believe it was pandas - that conflicted and needed to be updated locally to work with 3.9.12. This broke and raised an error in production that was not reproducible locally - and when you deploy on AWS, reproducing can be a pain in the butt. I'm sure if the data scientist had used perfect pyenv, virtualenv, and pip commands; we would have caught this. However, they're very complicated - especially for people who focus on math - so requiring full knowledge of these tools is unrealistic for most data scientists.