Top Data Science Jobs in San Francisco Bay Area, CA

As a Data Scientist, you'll enhance OpenAI's API solutions by defining key metrics, measuring model impacts, and improving developer experiences through analytics and cross-functional collaboration.

As a Data Scientist on the Financial Engineering team, you'll enhance payment systems, monitor analytics, and increase conversion rates while analyzing data for insights and decision-making.

Design measurement frameworks, build scalable analytics and AI-driven workflows, implement causal and Bayesian models, run experimentation (A/B, incrementality), partner with data engineering, and deliver executive dashboards to optimize growth marketing.

Work on monetization products (Subscriptions, Bits, Gifting) to design and analyze experiments, apply causal inference and quasi-experimental methods, build models for pricing/segmentation/revenue optimization, partner with product/engineering/finance, and create dashboards and reporting to inform product decisions.

Embedded on a core business team, define the right problems, build metrics and reports, run analyses and experiments, surface actionable insights, and drive data-informed decisions end-to-end across growth, product, AI, or partnerships.

Embed within a core business area to define problems, build metrics, run analyses and experiments, surface insights, and drive data-informed decisions end-to-end across growth, product, AI/automation, or partnerships.

Embedded data scientist owning analytics for a business unit: define problems, build metrics/reports/experiments, surface insights, and drive data-informed decisions across growth, product, AI, and partnerships.

Embed with a core business area to define problems, build metrics/reports, run analyses and experiments, and drive data-informed decisions end-to-end. Translate ambiguous questions into clear hypotheses, surface insights, and partner with product, growth, finance, and ops to execute recommendations and deliver measurable business impact.

Embedded on a central Data Science team, you will define problems, build metrics/reports, run analyses and experiments, surface insights, and drive decisions end-to-end for growth, product, AI/automation, or partnerships.

Embedded on a business unit, define analytic questions, build metrics/reports, run analyses and experiments, surface insights, and drive data-driven product, growth, AI, or partnership decisions end-to-end.

Develop predictive, optimization, and simulation models to forecast demand, allocate resources, plan maintenance, and evaluate network expansion. Build production-ready analytics and measurement frameworks, run experiments, identify operational risks, and partner with cross-functional teams to guide strategic operational decisions and improve network performance.

Lead and build Neural Earth's geospatial data science function: design and deploy Python-based geospatial models, architect production geospatial data pipelines, validate reproducible methods, translate scientific outputs into customer-ready products, and manage and mentor at least one direct report while advising customers and stakeholders.

The role involves developing graph algorithms, data pipelines for identity verification, and supporting data analysis for compliance products.

Analyze large marketing datasets, build predictive models for segmentation, pricing, and demand forecasting, support targeted campaigns and retention, partner with product and marketing, apply statistical and ML methods, ensure data quality with engineering, and communicate insights to stakeholders to drive conversion and retention improvements.

The Data Scientist at GenLogs will build machine-learning systems, analyze large datasets, and collaborate with engineering and product teams to deliver advanced logistics intelligence solutions.

Partner with Marketing as the embedded data science lead to build marketing measurement, attribution, LTV/ROAS/CAC models, run experiments, analyze the full funnel, create forecasts and dashboards, and influence channel strategy and budget allocation.

As the Staff Data Scientist, you will lead the data science function, driving strategy, mentoring peers, and shaping product direction through advanced analytics and experimentation.

This role involves analyzing EHR and insurance claims data to define clinical outcomes, developing AI models, and collaborating with technical experts for manuscript preparation.

Lead company-level measurement and causal strategy, define MAU×ARPU metrics and semantic mart, set scientific standards for experimentation and analysis, own strategic models (MMM, elasticity, forecasting), partner with engineering to productionize analytics, and raise org-wide scientific maturity through governance, templates, and upskilling.

As part of the Data Science and Analytics team, the Data Scientist will drive data-informed decision making and improve developer productivity by analyzing metrics, conducting causal analyses, and presenting actionable insights to stakeholders.

Lead and deliver end-to-end data science projects: design, build, deploy, and monitor ML/statistical models for credit risk, pricing, collections, and fraud. Partner with cross-functional teams to integrate models into production, set model development standards, mentor engineers, and maintain reliable ML pipelines and monitoring systems.

Analyze large, diverse datasets (claims, hazard, geospatial) and apply advanced data science, ML, and AI to model catastrophe, hazard, exposure, and man-made risks. Evaluate vendor models, develop internal risk views and tools, collaborate with domain experts, and support underwriting decision-making and account-specific risk analyses.

Build and maintain financial data models and scalable reporting systems powering ARR, revenue, billings, and other metrics. Partner with Finance, Accounting, Investor Relations, Engineering, and Data teams to ensure data accuracy across close processes, forecast and analyze performance, and operationalize key financial metrics to support executive decision-making and public company reporting.

Lead measurement and experimentation for Waymo growth marketing: design incrementality tests (geo holdouts, switchbacks), build LTV and CAC frameworks, run media mix modeling and deep-dive analyses across channels, audiences, creatives, and geos to optimize acquisition and connect marketing performance to business outcomes.

Develop, validate, and apply econometric and machine learning models to support utility clients. Clean and analyze large datasets, assess data sufficiency, perform quality control and peer code review, and present findings to nontechnical stakeholders. Balance rigorous modeling with clear written and verbal communication to inform client decisions.