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Baseten is a Series F AI infrastructure company ($1.5B valuation) that powers mission-critical inference for leading AI companies including Cursor, Notion, Abridge, and Writer. The company provides applied AI research, flexible infrastructure, and developer tooling to enable frontier AI companies to bring cutting-edge models into production.
You will be a founding Product Data Scientist, establishing how Baseten uses data to drive product decisions. This is a hands-on, foundational role reporting to founders and working directly with Product, Engineering, and GTM teams. You'll define what success looks like on a technical, usage-based platform and translate open-ended questions into analyses, forecasts, and experiments.
Key responsibilities include:
- Partner with Product and Engineering to frame critical questions, define success criteria, and translate analysis into roadmap and prioritization decisions
- Define product success metrics across activation, adoption, retention, expansion, reliability, and user experience
- Design measurement plans for launches, analyze A/B experiments and controlled rollouts, and drive product decisions from results
- Map enterprise customer journeys and measure feature adoption at each stage
- Evaluate releases and recovery by measuring traffic shifts, analyzing deployment behavior (warm-up, drain, probe, rollback), and tracking MTTR and self-serve incident outcomes
- Analyze customer and cohort behavior with clear recommendations
- Build source-of-truth reporting and self-serve analytics tools for cross-functional teams
You'll work with clickstream and product events, inference telemetry, and observability data to inform decisions on reliability, performance, adoption, and developer experience.
REQUIREMENTS:
- 5+ years of experience in product data science, product analytics, or quantitative roles, ideally supporting developer platforms, APIs, or B2B products
- Deep SQL and Python fluency with a track record of analyzing large event-level datasets and producing decision-ready work
- Strong statistical judgment and hands-on experimentation experience, including test design, power analysis, and knowing when directional evidence is sufficient to act
- Hands-on forecasting experience with ARIMA, Prophet, or comparable time-series methods, including disciplined backtesting, error analysis, and scenario planning
- Experience designing medallion data architectures (raw, conformed, business-ready models) with testing, documentation, and lineage
- Familiarity with dbt, semantic layers, data ontology, and BI tools such as Sigma or Hex
NICE TO HAVE:
- Experience with AI/ML infrastructure, model serving, GPU systems, or observability for distributed systems
- Experience with usage-based pricing, APIs, platform unit economics, capacity planning, or enterprise product analytics
- Experience with model-serving frameworks and inference engines such as vLLM, SGLang, and Dynamo
