I mention this in another reply but tl;dr the DS role is maturing and getting cleaved into different specialist skillsets. Research, ML Engineering, MLOps, Data Engineering, Analytics are what I see as the main ones. In 2016 you could get hired as a Data Scientist knowing some SQL, XGBoost, and how to use git (I know I did). That's not really the case in 2024, you are expected to be an expert in at least one of those areas and job titles are starting to reflect the specialization. Companies that are still hiring for generic "Data Scientist" positions with scikit-learn in the job description are not places you want to work.

The questions is how deep should you go of each subset ?