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

Lead data science initiatives supporting Product and Commercial Operations. Analyze growth opportunities, define success metrics, conduct experimentation and causal inference, and deliver actionable insights. Lead initiatives such as experimentation systems and data agents while promoting strong analytical practices. Partner with stakeholders to drive measurable product impact, minimize marketplace losses, and communicate complex findings to executive audiences.

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

Lead the development of statistical and machine learning models for time-series and geospatial data. Build analytic solutions, datasets, ETL workflows, forecasting and anomaly-detection models, simulations, visualizations, and reports. Evaluate algorithm performance, optimize classifiers, document processes, and collaborate with engineers, analysts, mission partners, and stakeholders to deliver reliable mission-focused software and data capabilities.

Lead statistical and machine learning model development for time-series and geospatial data supporting the FALCON mission. Responsibilities include feature engineering, forecasting, anomaly detection, ETL, dataset creation, algorithm evaluation, simulation, data visualization, reporting, documentation, and stakeholder collaboration. The role also contributes to software applications, APIs, data pipelines, code reviews, troubleshooting, and iterative agile development.

As a Senior Data Scientist, the role involves leveraging data analysis, experimentation, and statistical methods to influence business decisions and enhance Gusto's product performance, while mentoring team members.

Leads experimentation, causal inference, advanced statistical analysis, machine learning, and scalable data-product development. Partners with product, engineering, and research teams to generate insights, identify opportunities, influence executive decisions, and shape product strategy. Owns cross-functional projects, analytics roadmaps, reporting, stakeholder adoption, and methodological standards while translating complex data into actionable business outcomes.

Build actuarial and health-economic models (total cost of care, PMPM, MLR) from claims to project multi-year cost and utilization impacts, quantify uncertainty, support commercial pricing, defend analyses to payer actuaries, partner on causal/expt design, and produce outputs usable for pricing/reserving.

The role involves analyzing fraud and risk data, defining KPIs, collaborating on anti-fraud strategies, and creating tools and dashboards for data accessibility at Whatnot.

Develop and productionize AI and machine learning solutions for client, investment, and operational problems. Responsibilities include designing AI agents and agentic workflows, prototyping with foundation models and agent frameworks, building evaluation and observability systems, ensuring reliability and compliance, translating business needs into technical solutions, leading major initiatives, and presenting research and technical work to senior stakeholders.

Staff and Principal Product Data Scientists will use advanced analytics, experimentation, causal inference, machine learning, and large-scale data technologies to guide Slack product strategy. They will define success metrics, build self-service reporting and data models, evaluate product adoption drivers, identify growth opportunities, and help develop Slack’s agentic data delivery platform. The role partners closely with Product, Design, Engineering, Product Research, and go-to-market teams while communicating insights to executives and cross-functional stakeholders.

Leads market intelligence through advanced analytics, statistical modeling, machine learning, and AI/LLM-powered data products. Analyzes complex structured and unstructured data, builds scalable data pipelines and models, communicates insights to executives and business stakeholders, and mentors junior data scientists. Requires strong Python, SQL, data engineering, and AI agent development expertise, including output evaluation, guardrails, accuracy validation, and human-in-the-loop review.

Build statistical and machine learning models to optimize GPU utilization, inference capacity, latency, throughput, and fleet efficiency. Develop demand forecasts, analyze production bottlenecks, design experiments and simulations, and support infrastructure planning and GPU investment decisions. Create operational dashboards and communicate insights to engineering, product, finance, research, and executive stakeholders.

Lead marketing analytics and modeling to grow lending business: design experiments, build and validate campaign pipelines, measure funnel and ROI, manage direct-mail campaign selection and execution, create dashboards and provide data-driven recommendations to marketing and senior leadership.

Lead pricing modeling and experimentation to optimize margin, conversion, and retention. Build elasticity and willingness-to-pay models, design A/B and bandit experiments, develop pricing automation (DealBot) and monitoring for AI-driven rate recommendations, own ETL/analysis/visualization, and partner with Finance, Risk, Product, and GTM teams to operationalize pricing intelligence.

Design, develop, deploy, and monitor advanced deep learning models for fraud detection, risk management, and identity verification. Lead machine learning initiatives, research novel algorithms, manage end-to-end model lifecycles, collaborate with Product, Engineering, and Risk teams, and communicate insights to technical and executive stakeholders. Mentor data scientists and drive high-impact delivery, data quality, experimentation, and continuous improvement.

Lead data science initiatives for Risk and Support Operations by analyzing customer and product data, defining metrics, forecasting demand, evaluating experiments and risk policies, and building decision frameworks. Use AI tools and agent workflows to improve analytical efficiency. Partner with product, engineering, and risk teams, communicate insights to senior stakeholders, establish technical standards, mentor data scientists, and support hiring.

Own the analytical function for a business area such as growth, product, AI and automation, or strategic partnerships. Define business questions, metrics, reports, analyses, and experiments; surface insights; and drive data-informed decisions with cross-functional stakeholders. The role requires independent analytical problem-solving, SQL or Python proficiency, strong communication, and ownership of ambiguous initiatives through measurable business impact.

Own the analytical function for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; surface insights; and drive data-informed decisions and outcomes. The role requires independently structuring and interpreting data, communicating findings to leadership, and solving ambiguous problems using SQL or Python.

Analyze product, customer, risk, and support data to guide decisions across Block’s platforms. Own metrics, forecasting, staffing models, experiments, visualizations, and measurement frameworks. Evaluate risk models and policies for performance, bias, and drift; use AI tools and agent workflows to improve analytical execution. Partner with product, engineering, and risk stakeholders, lead technical standards, build reusable tooling, mentor data scientists, and contribute to hiring.

Leverage healthcare platform data to identify behavioral patterns, develop product and client-facing analytics, and guide data strategy. Lead data projects from ingestion through analysis and recommendations while collaborating with product managers, analysts, and developers. Apply advanced statistics, SQL, Python, distributed processing, machine learning, and data visualization to communicate actionable insights to stakeholders.

The Senior Data Scientist supports Customer Success through deep-dive analysis, predictive modeling, causal inference, experimentation, and KPI development. The role analyzes churn, retention, renewals, upsell opportunities, and revenue trends; diagnoses metric shifts; builds data pipelines and dashboards; and develops AI/LLM-powered solutions. The scientist collaborates with stakeholders, communicates insights, improves data quality, and measures the impact of customer success initiatives and product changes.

Lead development of statistical and machine learning models for time-series, geospatial, kinematic, and radar data. Build analytic solutions, training datasets, ETL workflows, forecasting and anomaly-detection models, simulations, visualizations, and performance metrics. Integrate multi-source sensor data, enhance algorithms, document processes, and collaborate with engineers, analysts, mission partners, and stakeholders in an agile environment.

Partner with Digital Assets, Finance, Treasury, and Risk to design measurement frameworks, perform strategic analysis and modeling, build scalable SQL-driven automation and dashboards, analyze on-chain and finance data, create visualizations, and drive data-informed product decisions for USDC and related digital asset offerings.

Owns product measurement and decision thresholds across Ascend’s financial operations platform. Responsibilities include defining accuracy, success, and exception metrics; auditing production scores; partnering with Product and Engineering on instrumentation; deploying models for confidence scoring, reconciliation, collections, and recovery; monitoring calibration, drift, and realized outcomes; designing experiments; and using product data to inform the roadmap.

Design and analyze rigorous messaging experiments using A/B testing and causal inference. Build measurement frameworks, data pipelines, dashboards, automated analyses, and scalable data solutions for messaging campaigns. Partner with CRM, Product, Algorithms, and Engineering teams to translate ambiguous business questions into actionable recommendations and experimentation standards. Support messaging strategy across channels, timing, content, targeting, and new business verticals.