Why I Keep Coming Back to Teaching Data Science

Alongside client-facing AI work, one throughline in my career has been curriculum design — founding the Data Science Modules and Nexus programs at UC Berkeley, and later co-founding Girls Who Solve to teach technical problem-solving to high school students.

Teaching forces clarity

Explaining a concept to someone encountering it for the first time forces a level of clarity that's easy to skip when you're only talking to other practitioners. Some of the clearest frameworks I've used in enterprise engagements started as a slide built for a classroom, not a boardroom.

Access shouldn't depend on which major you picked

The Data Science Modules program at Berkeley exists because data science shouldn't be gated behind a specific major or department. Supporting 10,000+ students meant meeting people where they were — literally, by embedding data science modules directly into other departments' existing courses.

The next generation of AI practitioners needs both technical and business fluency

Girls Who Solve was built on the same premise for high schoolers: technical problem-solving is more approachable, and more useful, when it's taught alongside the business context of why it matters. That combination — technical depth plus business fluency — is exactly what's made the difference in my own career.

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