How to build your data engineering/data science team?
summary
Explains how to structure teams: Series B startups run a central data science team while mature companies shift to a hub-and-spoke model with embedded analysts in business units. Maps the ML lifecycle from problem framing through feature pipelines, CI/CD deployment and production monitoring. Emphasizes iteration and automation to raise data quality and sharpen problem definition, and stresses non-deterministic deployments so teams implement pre-deployment testing and data distribution monitoring to detect drift and regressions.
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chapters (6)
- 0:00Introduction to team building
- 9:31Data science team roles
- 18:28Building quality products
- 36:25Team structure and skills
- 57:44Deployment and lifecycle
- 1:04:52Q&A and summary
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