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

Analyze acquisition data and guide marketing strategy, budget allocation, channel optimization, and growth decisions. Design incrementality experiments, develop marketing mix and allocation models, define acquisition metrics, identify new growth opportunities, and improve web conversion. Partner with Marketing, Engineering, Finance, Design, and User Research to translate statistical and machine learning insights into measurable business outcomes across paid, organic, consumer, and enterprise channels.

Embedded Data Scientist for Superhuman Mail, partnering with product, growth, engineering, and design teams to guide roadmap decisions. Responsibilities include defining product metrics, designing experiments, applying causal inference and machine learning, measuring AI feature quality, analyzing activation and retention, identifying growth and conversion drivers, and communicating insights to stakeholders. The role requires strong Python, SQL, experimentation, statistics, and cross-functional influence skills.

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.

Staff Data Scientist partnering with Growth, Marketing, Product, and Engineering teams to build data science solutions that drive customer acquisition, engagement, and retention. Responsibilities include predictive modeling, personalization, recommendation systems, LLM applications, incentive optimization, causal inference, marketing impact measurement, technical mentorship, and strategic leadership. The role requires deploying production machine learning systems and communicating analytical insights to senior stakeholders.

Own the reliability and quality of an AI copilot for trading platforms. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement loops. Partner with engineering and product on safe tool interactions, auditability, and user trust. Develop trading-domain expertise and measurement systems covering correctness, safety, latency, regression risk, and workflow performance.

Leads AI and GenAI data science workstreams for client engagements. Develops machine learning, deep learning, and NLP models; applies statistical and predictive modeling; designs scalable data science workflows and pipelines; evaluates model performance; communicates insights through visualizations and executive narratives; coaches teams; and supports responsible AI governance.

Develop and validate clinical heuristics and algorithms using real-world healthcare data. Build Python profiling pipelines, automated data-quality checks, and longitudinal patient cohorts. Analyze complex clinical databases and ETL pipelines, evaluate AI, NLP, and heuristic outputs, and collaborate with clinical, labeling, engineering, and data science teams. Maintain documentation, configuration, metrics, and validation standards for transparent, high-quality clinical analytics.

Develop data-driven insights and products for Salesforce’s MeshMesh networking platform. Responsibilities include product analytics, retention and behavioral analysis, data modeling, data instrumentation, algorithm deployment pipelines, self-service dashboards, and strategic advising of senior leadership. The role collaborates with Product, Infrastructure, Engineering, and data teams while promoting data-driven decision-making and improving organizational data practices.

Own the end-to-end data science lifecycle for payment optimization, including feature generation, model development, tuning, validation, experimentation, and deployment. Build experimentation frameworks to measure conversion, declines, fraud, and merchant impact. Collaborate with data engineering, BI, infrastructure, and product teams to develop scalable pipelines and models. This role establishes DEUNA’s data science practice and may develop into people leadership.

Leads health economics and outcomes research and real-world evidence projects for pharmaceutical, biotech, academic, and other partners. Designs and analyzes pharmacoepidemiologic studies, treatment patterns, healthcare utilization, causal inference, and survival analyses. Builds reusable analytical infrastructure, collaborates with scientists, engineers, and product teams, interprets results for stakeholders, supports publications and regulatory work, and ensures compliance with data governance and applicable regulations.

Build and operate supply chain data pipelines, models, integrations, quality controls, and dashboards supporting procurement, inventory, logistics, suppliers, and project analytics. Partner with analysts and business teams to enable forecasting, planning, scorecards, KPI tracking, and data governance. Contribute to cloud data architecture, engineering standards, tooling implementations, and productionizing analytics models across AI infrastructure and capital-intensive supply chain operations.

Build and deploy machine learning models for Waymo’s marketplace, including conversion, wait-time, retention, demand forecasting, pricing, matching, and vehicle positioning models. Apply reinforcement learning, design and evaluate experiments, collaborate with Engineering and Product, and present data-driven recommendations to leadership.

Build and evaluate fraud models for onboarding, login, account changes, and money movement. Develop labels, features, sampling strategies, thresholds, and production monitoring to detect account takeover, scams, money mules, and identity fraud. Transition third-party risk models to in-house solutions, measure model performance and drift, collaborate with fraud, risk, operations, and compliance teams, and evaluate AI agent workflows for transaction review and fraud-ring analysis.

Lead product analytics and data science for a member platform, shaping product strategy through behavioral analysis, experimentation, segmentation, retention and churn modeling. Own product metrics end to end, build trusted datasets using Fivetran, dbt and BigQuery, and implement testing, monitoring, lineage and documentation. Partner cross-functionally with product, engineering, finance, operations and go-to-market teams to identify growth opportunities and support roadmap decisions.

Leads the design, development, testing, and evaluation of AI and machine learning solutions. Directs data analysis, modeling, infrastructure, monitoring, and integration efforts while establishing standards for AI robustness, bias, explainability, failure modes, and technical controls. Manages large projects, advises clients, coaches teams, and develops reusable evaluation tools and evidence workflows for responsible AI deployment.

Lead data science projects from discovery through delivery, applying statistical modeling, forecasting, experimentation, and AI to optimize pricing, resource allocation, operations, and customer experiences. Build analytical workflows and AI-enabled applications using Python and SQL, collaborate with Engineering on deployment and monitoring, and communicate data-driven recommendations and trade-offs to business and technical stakeholders.

Own the analytics lifecycle from data ingestion and modeling through BI visualization and decision enablement. Build SQL/Python analyses, automate ROI reporting, define metrics, develop scalable ETL pipelines and data warehouses, and deliver ThoughtSpot dashboards. Improve data quality through validation and monitoring, collaborate with Product and Engineering, and communicate actionable insights to technical and non-technical stakeholders. Participate in customer-facing ROI presentations and prototype ML models to inform product decisions.

Own production Python and PySpark machine learning solutions for large-scale advertising and viewership data. Design measurement, attribution, and causal models; partner with Data Engineering on scalable pipelines; lead technical reviews; mentor data scientists; and communicate findings to technical, executive, and client audiences.

Lead data science efforts to inform product and business strategy through quantitative analysis, experimentation, forecasting, and storytelling. Partner with cross-functional teams to define hypotheses, measure impact, influence roadmaps, present insights to senior leadership, and monitor key product metrics to drive value.

Develop and deploy statistical and machine learning models for credit risk, delinquency, defaults, loan losses, underwriting, and risk-based pricing. Analyze credit bureau, alternative, and loan performance data; build production-ready analytical pipelines; monitor model performance; and address data anomalies. Lead complex initiatives, mentor colleagues, document methodologies, and incorporate model governance, fair lending, privacy, and responsible AI considerations while partnering with Credit Risk, Product, Engineering, and Finance.

Leads payer healthcare data science and operations consulting initiatives, using business analytics to improve payer processes and performance. Manages teams, client relationships, project resources, strategic planning, process optimization, quality standards, and operational transformation. Coaches junior staff, promotes innovative technologies, and identifies opportunities to enhance operational excellence and business outcomes.

Lead forecasting initiatives by building, deploying, monitoring, and maintaining interpretable statistical and machine learning models. Develop production-ready forecasts for user growth, revenue, compute consumption, customer lifetime value, and profitability. Create self-service forecasting tools, evaluate emerging forecasting methods, translate technical results into strategic recommendations, and collaborate with product, engineering, research, finance, and executive stakeholders.

Senior Data Scientist partnering with Product, Engineering, Design, Operations, and Clinical teams to define metrics, design experiments, apply causal inference, analyze funnels and cohorts, and build predictive models. The role translates complex data into actionable product and business recommendations, operationalizes validated insights, and establishes standards for experimentation and analytical quality across patient, payer, outcomes, and provider-focused teams.

Leads data science and advanced analytics initiatives for Auto Claims by developing machine learning and predictive modeling algorithms, integrating structured and unstructured data, and creating tools to support business decisions. Communicates modeling results to stakeholders, researches emerging techniques, manages moderately complex projects, and provides mentorship to junior data scientists. The role is remote with up to 10% travel to regional offices.

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.