Senior Data Scientist, Core Product

Glean: Glean•San Francisco, CA

1d•Hybrid

About The Position

This role sits on the Core Product Data Science team within R&D, part of a world-class organization spanning data science, applied science, data engineering, and analytics. Based in the Bay Area (San Francisco or South Bay), you'll be embedded at the center of where enterprise AI is heading. You'll partner with Engineering, Product, and Design to own measurement, insight, and impact across the Glean Assistant — one of the most interesting product surfaces in enterprise AI today: Define what "good" looks like for AI — build the KPIs, pipelines, and dashboards that let the team move with conviction; lead logging improvements that turn nascent telemetry into a reliable foundation for decision-making Redefine user engagement for an AI-first product — measuring value when AI is doing half the work is an unsolved problem; you'll be on the frontier of figuring it out Scale analytics access — develop data models and self-serve tools so cross-functional partners can move fast from a shared, accurate source of truth — without a bottleneck Shape what gets built — identify what's working, what isn't, and why; influence roadmaps with quantitative frameworks and rigorous assessments of product-market fit Work on surfaces that are moving fast — Glean Chat evolving from informing users to co-performing complex work alongside them; new job types (document generation, data analysis, software engineering), new settings (meetings), new modalities (voice, image), and multi-user experiences This is a rare opportunity to do foundational data science work on AI products that are actually deployed at scale inside real organizations — not a research prototype, not a side bet. If you want to work at the center of where enterprise AI is heading, this is the role.

Requirements

6+ years of experience in a quantitative data science role (3+ years with a PhD) in Statistics, Mathematics, Computer Science, or a related field
Strong SQL and Python skills; comfortable with modern data stacks (dbt, analytics engineering pipelines)
Fluent in AI tools as part of your everyday workflow
Product and business mindset — you know how to define KPIs, set guardrails, and build dashboards that actually drive decisions
Solid statistics foundation, including experimentation and causal inference
Proven ability to work with messy, early-stage data and turn it into clear, actionable insights
End-to-end ownership: from problem framing to delivery, you drive work independently
Clear communicator across technical and non-technical audiences
B2B SaaS experience, ideally in enterprise AI
You are excited about improving internal tooling/processes in Data Science teams to make others more productive, e.g. improve AI adoption, improve A/B testing tooling etc.

Responsibilities

Define what "good" looks like for AI — build the KPIs, pipelines, and dashboards that let the team move with conviction
Lead logging improvements that turn nascent telemetry into a reliable foundation for decision-making
Redefine user engagement for an AI-first product — measuring value when AI is doing half the work is an unsolved problem; you'll be on the frontier of figuring it out
Scale analytics access — develop data models and self-serve tools so cross-functional partners can move fast from a shared, accurate source of truth — without a bottleneck
Shape what gets built — identify what's working, what isn't, and why; influence roadmaps with quantitative frameworks and rigorous assessments of product-market fit
Work on surfaces that are moving fast — Glean Chat evolving from informing users to co-performing complex work alongside them; new job types (document generation, data analysis, software engineering), new settings (meetings), new modalities (voice, image), and multi-user experiences