Senior Data Scientist, Product Intelligence
Fal - San Francisco
Posted Jan 14, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Salary
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- 401(k) match
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Schedule
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- Weekend work
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Application
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- Deadline
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Where they hire
State eligibility is not yet verified.
About this role
Senior Data Scientist, Product Intelligence San Francisco fal is the generative media ecosystem powering the next generation of AI products. We build the infrastructure, tools, and model access that teams need to move from idea to production, and do it at scale without compromise. For developers and enterprises, fal is the foundation that makes generative media not just possible, but practical: a unified platform where high-performance inference, orchestration, and observability come together to unlock new categories of AI-native products. As generative media reshapes industries across a market projected to grow by hundreds of billions over the next decade, fal is becoming the ecosystem that ambitious teams build on. About this role: As a Senior Data Scientist for Product Intelligence, you will architect the analytical foundation for how fal builds and grows. Your domain spans the entirety of the developer journey-from initial acquisition to long-term expansion. You will turn raw behavioral and system signals into the metrics and insights that shape our product roadmap and go-to-market strategy. This is a high-influence role where you will act as a strategic partner to Product, Engineering, and Marketing. Beyond domain-specific analysis, you will share ownership of the foundational data systems, standards, and experimentation frameworks that serve as the source of truth for the entire company. What you'll do - Architect the metrics that define user success on fal and build the measurement systems to track activation, retention, and time-to-value across both our self-serve and B2B pipelines. - Develop a deep, causal understanding of how
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