On Project management for Data Science

An end-to-end approach to manage projects with a Machine Learning component

A couple of years ago, I moved in my job from the most experienced team on building solutions with Machine Learning to a team eager to have its first wave of models.

We, the “Data Science people”, did a brief document about the model and explained it to the team in a meeting. Soon, we started grabbing the data and fitting some models. After we provided the first results - still as model metrics, someone cheered in a team retrospective: “We are close to having ML models!”. I had to adjust the expectations, and we agreed to clarify the project scope for the team.

The Product Manager set a meeting with everybody, so we could write a PRD (Product Requirements Document) to define the project scope. Then I had this great revelation. I was amazed by how different ideas people had about what we would build. That was one of the best things I could have been exposed to learn about the importance of shared understanding.

After a confusing hour, I approached an Agilist to get some help. He recommended to me Jeff Patton’s User Story Mapping book 1. The book is excellent. As I applied it to Data Science projects, I could identify the key parts of it for the field and develop others with the team.

I primarly thought about writing it as a blog post, but as it required more and more structure, I’ve decided to have it in a new section called Data Science Management since that enables continuous improvement by adding content relevant to Data Science Management in general.


  1. Patton, J., & Economy, P. (2014). User story mapping: discover the whole story, build the right product. : O’Reilly Media, Inc.