Product Data Scientist

San Francisco · Remotefull timeRemote

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mid

$185k–$260k

H1B: no

Baseten Product Data Scientist at Baseten responsible for defining product success metrics and supporting experimentation and launches. This role involves analyzing large event-level datasets and turning ambiguous questions into actionable insights to shape product strategy.

Responsibilities

We're hiring a Product Data Scientist to establish how product decisions at Baseten are made with data. You'll work directly with Product and Engineering, alongside GTM to determine measurement, strategy, experimentation and implementation.
This is a foundational, hands-on role. You'll define what success looks like across a technical, usage-based platform and turn ambiguous questions into analyses, forecasts, and experiments that shape product strategy. You'll work from clickstream and product events through inference telemetry and observability data, helping Baseten make faster decisions about reliability, performance, adoption and developer experience.
Partner directly with Product and Engineering: frame the questions that matter, define success criteria, and turn analysis into roadmap, launch, and prioritization decisions.
Define how product success is measured: establish metrics across activation, adoption, retention, expansion, reliability and user experience.
Support experimentation and launches: design measurement plans, analyze A/B experiments and controlled rollouts, and translate results into product decisions.
Diagnose reliability and scaling behavior: join customer signals with request, replica, deployment, and cluster telemetry to find patterns in release bottlenecks, unhealthy replicas, and models without traffic.
Define the enterprise customer journey and measure feature adoption along the way
Evaluate releases and recovery: measure traffic shifts, evaluate warm-up, drain, probe, and rollback behavior and track MTTR and self-serve incident outcomes.
Turn insights into action: analyze customer and cohort behavior, build source-of-truth reporting and self-serve tools, and communicate clear recommendations.

Qualification

Deep SQL and Python fluencyHands-on forecasting expertiseFamiliarity with dbtExperience with AI/ML infrastructureExperience with usage-based pricing

Required

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 practical experimentation experience, including test design, power analysis and knowing when directional evidence is sufficient to act.
Hands-on forecasting expertise, including ARIMA, Prophet, or comparable time-series methods, with 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.

Preferred

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, and/or enterprise product analytics.
Experience with model-serving frameworks and inference engines including vLLM, SGLang and Dynamo.