Building A Successful Data Science Team
summary
Claims three concrete drivers of success: three distinguishing practices — hiring complementary roles (data scientists, data engineers, storytellers), prioritizing use cases for quick measurable value, and rapid technical enablement. Cites tooling like pandas, scikit‑learn, Spark, NLP and computer vision and warns that over 8 out of 10 projects fail to reach production. Describes an internal prototype combining mobile AI Spark NLP and CV built in 2019 that functions as a demo and illustrates that the final narrative and integration — the "last 20 minutes on the grill" — determine whether insights turn into paid value.
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chapters (6)
- 0:00Building a data science team
- 2:51Three keys to success
- 8:11Balanced team roles
- 12:30Picking early use cases
- 17:48Rapid team enablement
- 23:49Closing thoughts & Q&A
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