There’s no one-size-fits-all answer here, but generally, data scientists spend about 70% of their time on data wrangling, which is the phase of the data science pipeline that focuses on gathering, cleaning, and preparing data for use. Another 30% of their time is spent on model development, and the remaining time is spent on post-deployment efforts like upkeep, visualizations, storytelling, etc.