Top Data Science Jobs in San Francisco Bay Area, CA

As a founding Data Scientist/Machine Learning Engineer, you'll develop AI/ML models, enhance product capability, and drive impactful user outcomes while working closely with product teams.

Own revenue and product analytics: build KPIs, dashboards, dbt-style data models, run product/usage experiments, partner with GTM, and deliver executive insights to scale revenue operations.

The Senior Data Scientist will leverage data insights to drive growth in lending, foster AI integration, and mentor colleagues while ensuring high-quality data reporting and visualization.

The Senior Data Scientist will manage end-to-end data science efforts, ensure stakeholder trust through timely analysis, and build systems for reproducibility while collaborating with various teams.

Own the analytics lifecycle: build SQL/Python analyses and ETL, define metrics and reusable data models, produce customer-facing ThoughtSpot dashboards and ROI analyses, prototype ML solutions, improve data quality, and design and maintain the analytics data warehouse and pipelines.

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.

As a Data Scientist on Codex, you will enhance AI developer tools by defining developer productivity, running experiments, and analyzing user metrics to guide product direction.

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.

Drive pricing and yield optimization at GoFundMe by applying economics, behavioral science, and machine learning. Lead experimentation, model decision-making, and translate insights into action, while ensuring high standards for modeling and collaboration with product teams.

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

Own People & Talent data domain: design and govern HR/recruiting data models and pipelines, build dashboards and self-serve analytics, partner with cross-functional teams, enable AI-powered insights, and improve automation, data quality, and access for global people decisions.

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.

Partner with Brand Marketing and cross-functional teams to build end-to-end measurement frameworks, attribution and causal models, and dashboards that evaluate brand and channel impact. Apply statistical and ML techniques to audience targeting, creative effectiveness, and campaign measurement, and translate insights into strategic recommendations to optimize marketing spend and improve measurement infrastructure.

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.

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.

This role involves onboarding clients, solving scientific problems, guiding data analysis on the Tempus platform, and creating documentation. Requires expertise in biomedical datasets and programming.

The Data Scientist will analyze data to enhance business performance, engage customers, and optimize product strategies through experimentation and metrics establishment.

Lead data-driven strategy for Pinterest Ads by translating business needs into analytical solutions, ensuring data quality, connecting product and financial performance, advising cross-functional leaders, and elevating data science practices across the organization.

Build, test, deploy, and monitor ML models for credit risk, fraud detection, and transaction timing. Create training datasets, run experiments and A/B tests, analyze customer trends, and collaborate with Engineering, Product, and Business teams to deliver end-to-end data science solutions.

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.

The Data Scientist will build predictive models, drive forecasting, design experiments, and translate findings for stakeholders, optimizing healthcare operations.

Perform statistical analysis, predictive modeling, and data visualization on workforce and training data. Build dashboards, produce ad hoc analyses and decision-support products, advise on data governance/privacy, and collaborate with stakeholders to define metrics, deliverables, and reproducible analytic workflows.