There is a lot of information available about best practices so data scientists can build analytic models, but much less about how to manage analytic projects and analytic teams and how to coordinate with other departments within a company so that analytic models can be deployed and integrated into a company’s products, services or operations.
On May 23, 2019, I gave a talk at The Data Science Conference (TDSC) in Boston about managing analytic projects and their teams.
I focused on two themes:
- How to quantify the risk of an analytic project and work to reduce it over time.
- Understanding how to recruit a balanced data science team, which makes it more likely that your project will be successful.