Top Senior Data Science Jobs in San Francisco Bay Area, CA

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.

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.

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.

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.

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.

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

Lead product-focused analytics and experimentation to improve core experience and AI initiatives. Build propensity models and segmentation, create automated dashboards and executive reports, run and monitor experiments, and partner cross-functionally to translate analysis into business impact and drive product decisions.

Lead SMB Ads analytics end-to-end: define growth KPIs, diagnose funnel friction, build propensity/LTV models, design attribution and A/B experiments, automate dashboards, and forecast advertiser/revenue growth to inform product and marketing decisions.

Lead measurement and insights for ads demand: define metrics and benchmarks, analyze advertiser performance, build early-warning signals, design experiments, translate findings into sales and product actions, forecast opportunities, and close feedback loops to improve advertiser outcomes.

The Staff Data Scientist will lead the development and deployment of ML and GenAI systems to detect billing errors and fraud in healthcare payments, collaborating with cross-functional teams.

Lead the fraud data science function: define ML strategy, build and deploy real-time fraud detection models, investigate fraud patterns (including lending fraud), run experiments, collaborate with stakeholders, and maintain monitoring and dashboards to minimize fraud losses.

Lead Fraud Data Scientist to define ML strategy, build and productionize fraud detection models (supervised and unsupervised), investigate emerging fraud patterns, run experiments, collaborate with Product/Engineering/Risk, and maintain dashboards tracking fraud KPIs.

Lead the fraud data science function: define ML strategy, build and productionize fraud and lending-fraud models, run advanced analyses (clustering, network analysis), design A/B tests, integrate ML with rules engines, monitor models in cloud, and communicate results to stakeholders.

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.

Seeking a skilled Data Scientist/Machine Learning Engineer to develop machine learning models, design AI systems, and collaborate in data analytics. Responsibilities include productionizing models, data engineering, and cross-functional collaboration.

Lead data science across autonomy, safety, and product by analyzing machine telemetry and operational data, defining metrics, designing experiments, and partnering with ML platform and engineering to deliver insights that improve system performance and safety.

As the first Data Scientist, you will analyze user data and AI outputs to enhance content creation, define metrics, conduct A/B testing, and collaborate closely with cross-functional teams to improve product features.

Build and operate GTM intelligence decision products: define features, methods, pipelines, monitoring, and evaluations; partner with GTM stakeholders to prioritize accounts, surface risks and opportunities, ship production Python/SQL workflows, and iterate using field feedback and outcomes.

As a Senior Data Scientist, you'll optimize search relevance, retrieve content, analyze data, and collaborate with teams to enhance product strategies.

Partner with product, engineering, design, ops, and clinical teams to define metrics and experiments. Design and analyze A/B tests and quasi-experiments, apply causal inference when needed, perform funnel/cohort analyses, build and validate predictive models, and operationalize insights with monitoring and launch criteria. Produce clear, decision-ready analyses and raise organizational standards for metrics and experimentation.

As a Data Scientist at Anthropic, you'll support data-driven decisions across the customer lifecycle by defining metrics, analyzing data, and developing statistical models. You'll collaborate with various teams to influence strategies and improve analytics infrastructure.

Founding Data Scientist for Procure-to-Pay working cross-functionally to build models, metrics, simple data pipelines, dashboards, and reports. Analyze complex datasets, produce actionable insights through data storytelling, and partner with leadership to turn business problems into data-driven solutions that impact operations and growth.

The Data Scientist role involves developing measurement models, writing production-quality code in Python, and collaborating with teams to optimize data pipelines and ML workflows.

Lead development of automated, auditable revenue data pipelines and anomaly detection to improve reconciliation and data integrity. Build reusable financial data assets, dashboards, and reports; collaborate with finance and cross-functional teams to support month/quarter-end close and audit readiness.