Full Time

Lead Data Scientist at Gamma

Company Gamma
Location San Francisco
Salary $180K - $310K
How You'll Work onsite
Level senior
Sector Technology
Posted Posted 0 days ago

Job Description

You'll be Gamma's first Data Scientist, turning massive volumes of user data and AI outputs into insights that shape how millions of people create content. With over 1 million AI-generated presentations and 5 million AI images created daily, the signal is enormous. Your job is to find the patterns, measure what matters, and help us ship better features faster.

You'll design A/B tests that measure product impact, build frameworks that reveal how our AI models perform across user segments, and investigate the hard questions: what makes a good AI-generated presentation, and why does a feature land differently for enterprise versus consumer users? You'll partner closely with product, engineering, and design to define quality metrics, uncover edge cases, and guide decisions with data. This is an IC role for someone eager to be hands-on, but it can grow into a leadership position establishing the Data function at Gamma.

You'll thrive here if you're curious, comfortable with ambiguity, and excited to figure out what questions to ask rather than just answering the ones given to you.

Our team has a strong in-office culture and works in person 4–5 days per week in San Francisco. We love working together to stay creative and connected, with flexibility to work from home when focus matters most.

Responsibilities

  • Build frameworks that help the team understand AI model performance, user behavior, and product health across Gamma's platform
  • Dig into AI model outputs across user cohorts to identify quality gaps and create evals and metrics to measure improvement
  • Partner with engineering and product to define quality metrics for AI-generated content and user satisfaction
  • Develop statistical models and frameworks that empower product teams to make data-informed decisions independently
  • Design and analyze large-scale A/B tests and experiments with statistical rigor to measure product impact and guide prioritization

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