Career Panel
summary
Panelists compare concrete career moves and results: rebuilding municipal datasets after Katrina when eighty percent of the city had flooded, creating a public platform that aggregates police use-of-force records that moved policy conversations, and choosing applied tracks like an industrial math masteries to get into ML product work. Hiring focuses on candidates who ask about users and question overfitting rather than reciting benchmarks. Teams at Spotify combine mixed methods user research with data science to translate model metrics into product changes that actually move business outcomes.
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chapters (21)
- 0:00Introduction
- 1:37How did you choose your career
- 3:23How did you wind up academia
- 5:40How did you get a Masters degree
- 8:02Academic credentials and pedigree
- 9:16Data science in traditional industries
- 10:04Data science in local government
- 11:08How do you find talent
- 13:06Questioning mindset
- 13:36How teams get constructed
- 16:00Advocacy vs mentorship
- 16:41Examples of advocates
- 18:02Examples of white men as advocates
- 20:16Mentors
- 21:26Remini
- 22:43Imposter Syndrome
- 24:52Machine Learning
- 26:28Expertise vs Openness
- 30:25Questions
- 33:53Feedback
- 36:22Speed Round
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