Data Science Dysfunctions

Twelve HR-Ready Roles to Help Build Healthy Data Science and AI Teams

Put those data science Venn diagrams away. Yes all thirteen of them.

Photo by Artem Podrez

The ongoing great resignation that started in 2021, while gathering steam and backed by credible data, is nothing new in data science.

Today is not about running explainability on why people are quitting. (We did this and bravely published our thoughts on how we think this can be done with empathy and governance). Rather today I want to focus on one key driver of dysfunctional data science teams — management mistyping what data scientists should and should not do.

This particular dysfunction leads to multiple failure modes, including:

• Data scientists and analysts being pressed into building full stack applications instead of working with software engineering (in fact multiple roles in software engineering).
• Data scientists taking responsibility for data management, reporting and building dashboards (instead of working with professional data engineering and business intelligence teams).
• AI researchers spending increasing chunks of time deploying and monitoring models once projects are complete (this is closer to the work…