Data science as a team sport
summary
Gives concrete criteria for growing a data team: hire when scope, speed, or foundational tech debt overwhelm one person; aim for 5–10 data scientists with about 3 data engineers for a mid‑sized product org; add specialization, formal processes (try a documentation party), metadata/provenance and automation to reduce human error, and build resilience with onboarding, diversity, juniors/interns and planned failure testing inspired by chaos monkey.
AI-written summary · about
chapters (10)
- 0:00Intro
- 2:34When do you grow
- 3:52Expanding your team
- 12:13Being clueless
- 14:19Failure
- 15:02Hiring experts
- 15:40Resilience
- 17:05Teams dont scale
- 18:26Excellence isnt binary
- 18:42Questions
related talks
30mBuilding the best data team is not rocket science
31mProductionalizing a Data Science Team
16mRedefining Agile for Data Science: A Team First Approach
29mBuilding successful data teams in the realities of 2023
54mThe secret sauce of data science management
44mPeople, Technology, Culture: How to make Data Science work
38mBuilding Effective Data Teams: Strategies for Success
30mBuilding flexible data teams to improve product delivery