There are few frameworks on effective AI Project Management. Industry-standard frameworks for data analysis projects, like CRISP-DM, exist but none are effective for managing the development of AI products from deployment to production. The result is that many data science teams are focused on outputting one-off analytical projects, rather than building long-term, maintainable products that directly drive business processes and goals. In this talk we propose an effective project management framework for building production data science systems based on our experience constructing such systems at large tech companies, late-stage start-ups, and early-stage ventures. Specifically, we propose that a blend of CRISP-DM and the Extreme Programming agile development methodology can serve as a starting point for building organization-specific data science product development processes.