Product Data Scientist

Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We recently raised our $1.5B Series F, led by Altimeter Capital, Conviction Partners, and Spark Capital.

• Partner with Product and Engineering to frame the questions that matter, define success criteria, and turn analysis into roadmap, launch, 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 turn the results into product decisions.
• Map the enterprise customer journey and measure feature adoption at each stage.
• Evaluate releases and recovery by measuring traffic shifts, analyzing warm-up, drain, probe, and rollback behavior, and tracking MTTR and self-serve incident outcomes.
• Analyze customer and cohort behavior and share clear recommendations.
• Build source-of-truth reporting and self-serve tools that teams across Baseten can rely on.

• 5+ years of experience in product data science, product analytics, or another quantitative role, 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 enough to act on.
• 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, including raw, conformed, and 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.
• Nice to have: experience with usage-based pricing, APIs, platform unit economics, capacity planning, or enterprise product analytics.
• Nice to have: experience with model-serving frameworks and inference engines such as vLLM, SGLang, and Dynamo.
• Benefits: competitive compensation including meaningful equity; 100% coverage of medical, dental, and vision insurance for employee and dependents (U.S. only); flexible PTO including company-wide Winter Break; paid parental leave; fertility and family-building stipend through Carrot; company-facilitated 401(k) (U.S. only).