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

Own analytical strategy for enterprise knowledge-worker adoption across ChatGPT Work and Codex. Define measurement frameworks for activation, retention, workflow depth, and monetization. Analyze behavioral and customer evidence to identify personas, use cases, and interventions that drive durable usage. Design experiments and quasi-experiments, operationalize datasets and scorecards, improve instrumentation with Analytics Engineering, and translate insights into recommendations for Product, GTM, Finance, and executives.

Lead design, execution, and interpretation of A/B tests and quasi-experiments; apply causal inference methods; build centralized experimentation frameworks; extract and analyze data with SQL and Python; mentor data scientists and communicate results to stakeholders and senior leadership.

Design, build, and deploy predictive GTM models (LTV, attribution, propensity). Lead experimentation and causal analyses, define KPI measurement frameworks, partner with Data Engineering, and translate statistical insights into actionable recommendations for Sales, Marketing, and Customer Success.

Lead a 5–8 person Data Science team to develop and operationalize FWA detection models using SAS and Python on multi‑payer healthcare claims. Oversee model development, data pipelines, quality reviews, stakeholder presentations, and collaboration with SMEs and BI developers. Split role ~40% management, 60% hands-on coding/analysis.

Lead GTM-focused data science initiatives: build revenue forecasts, apply causal inference, develop AI-enabled sales and retention solutions, partner with Product/Growth/Commercial teams, and translate analyses into scalable, automated workflows and executive-ready recommendations.

Partner with Growth, Marketing, Sales, and Product to measure and optimize marketing and growth initiatives. Build dashboards and scalable measurement frameworks, design experiments, analyze ROI and trends, provide actionable recommendations, and communicate results to technical and non-technical stakeholders.

Partner with product teams to define and govern success metrics, design and evaluate experiments, build dashboards and data models, validate and prepare analysis-ready data, and translate analyses into actionable recommendations to guide product decisions and scale analytics practices.

Lead cross-functional data science initiatives for marketing, growth, and product. Design causal measurement and experimentation frameworks, build ML and uplift models for personalization and allocation, improve data quality and instrumentation, use AI-assisted tools to accelerate analysis, and mentor other data scientists while delivering clear, executive-level recommendations.

As a Staff Data Scientist, you will lead initiatives to build and optimize machine learning models, collaborate across departments, and solve complex business problems using data.

As a Data Scientist, you will build predictive models, develop software tools, and analyze geoscientific data to support mineral exploration and discovery.

As a Data Scientist, you will develop predictive models, analyze geoscientific data, and collaborate with geoscientists to improve mineral exploration efforts.

As a Data Scientist in Marketing, you will enhance AI-driven marketing strategies, collaborate with teams to develop metrics, analyze marketing data, and recommend improvements for user engagement.

Lead development, maintenance, and monitoring of credit risk models for lending portfolios. Use SQL and Python to build pipelines, perform EDA, forecast losses, automate dashboards, produce audit-ready documentation, support model governance/validation, and communicate insights to stakeholders.

Provide analytics and data-driven insights for CoverMyMeds clients, prioritize work that drives commercial outcomes, build relationships with account teams, apply statistical and predictive methods, monitor portfolio health, deliver client-facing visuals and recommendations, support complex requests and escalations, and inform strategic decisions across product, analytics, and operations.

Lead measurement and experimentation for the Identity product, define metrics and frameworks, design A/B tests and causal analyses, build datasets and dashboards, quantify growth vs. abuse trade-offs, and partner cross-functionally to inform product strategy.

Lead end-to-end data science initiatives, developing AI solutions for personalization, marketing, and product experience while collaborating with cross-functional teams on machine learning models.

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.

Develop frameworks and tooling to estimate long-term impacts of product changes from short-term experiment outcomes. Build causal inference methods (experimental, econometric, quasi-experimental), evaluate heterogeneous treatment effects, own major estimation problems, and communicate results to technical and non-technical stakeholders to drive better long-term decisions.

The Sr. Football Data Scientist will build, deploy, and refine analytical models for football insights, managing large datasets and collaborating with teams for high-quality analytics solutions.

Lead end-to-end data science for a priority vertical (Insurance), building, validating, and deploying ML models that drive ROAS and lead quality. Partner with stakeholders and ML engineering to monitor live performance, iterate models, and identify new modeling opportunities to improve revenue and media efficiency.

This role involves leading data science projects, designing technical solutions, implementing machine learning methodologies, and developing data workflows for cybersecurity applications.

The Bioinformatics Data Scientist will analyze multi-omics data, develop methods, build tools, and communicate findings to support drug development decisions.

As a Senior Data Scientist, you will lead high-complexity projects, develop ML and NLP solutions, and collaborate across teams to drive business impact through statistical modeling and data analysis.

Lead pricing analytics as the first dedicated data scientist on the Pricing team. Design analyses, causal models, experiments, and decision frameworks linking usage, conversion, retention, and revenue to guide pricing, packaging, discounts, and monetization across consumer, SMB, and enterprise segments. Communicate executive-ready recommendations and build a repeatable pricing data science function.

Develop and maintain predictive pricing, retention, and conversion models using GLMs and advanced analytics; build monitoring tools for rating performance; support rate filings, product pricing analyses, competitor research, and cross-functional collaboration to inform pricing and enterprise risk decisions.