Data Science doesn’t fit neatly into agile/software processes, but many are still valuable.

We use:
– Daily stand ups
– Planning, grooming, retro meetings
– Sprints
– Jira tickets
– Estimation
– Peer reviews

Adaptations we’ve made:
– 1 week sprints work better for us than 2 week sprints

– We rely heavily on timeboxing, because “definition of done” can be difficult at certain stages of the data science process

– We added a “design review” meeting to discuss DS approaches for active projects

– We don’t overthink estimation. We work to be directionally right, but focus most of our time on solving the problems in front of us.

If you’re re-thinking your Data Science process, start with familiar processes and then freely adapt them to your needs. 

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