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

Build and deploy statistical and machine-learning models for forecasting, risk prediction, retention, no-show prediction, experimentation, causal inference, and clinical outcomes. Partner with analytics engineering and technology teams to productionize models using monitored data pipelines. Translate ambiguous clinical and operational questions into rigorous analyses, communicate recommendations to cross-functional stakeholders, and establish data science and responsible AI practices.

Develop fraud detection and identity-risk machine learning models from problem definition through production deployment. Responsibilities include investigating fraud patterns, exploratory analysis, feature engineering, model training and evaluation, production-quality Python and PySpark development, monitoring model and feature drift, and presenting analytical recommendations. The role requires collaboration with data scientists and engineers while addressing privacy, explainability, validation, governance, and real-world fraud-modeling challenges such as class imbalance and delayed labels.

Analyze modeled outputs and research data to produce automated reporting, visualizations, statistical insights, and actionable guidance for political strategists and clients. Build analytics pipelines, conduct regression-based analysis, monitor research returns, identify data quality issues, and collaborate with engineers, machine learning scientists, statisticians, and political experts.

Lead the design, development, deployment, monitoring, and improvement of interconnected customer lifetime value models covering conversion, retention, premiums, and claim losses. Serve as a hands-on technical leader, guide modeling strategy, evaluate experiments and business impact, partner with engineering and business teams, support production ML systems, establish technical standards, and coach other data scientists.

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.

Develop and operationalize machine learning and statistical solutions for pharmaceutical development and manufacturing. Responsibilities include predictive modeling, computer vision, exploratory analysis, model validation, deployment, monitoring, software package development, testing, documentation, and model governance. The role translates scientific and business challenges into scalable applications, collaborates with multidisciplinary teams, supports AI/ML projects, and communicates findings to scientific and cross-functional stakeholders.

Develop data pipelines, organize and analyze structured and unstructured government data, create visualizations, build AI agents and lightweight automations, and occasionally develop predictive models. The role involves consulting with senior stakeholders, communicating actionable insights, supporting data capacity building, and collaborating with product, design, engineering, and research teams in a remote government-services environment.

Develop machine learning and statistical models to identify, measure, and manage credit and fraud risk. Partner with stakeholders to translate requirements into scalable solutions, analyze large application and transaction datasets, deploy end-to-end models, and improve risk decision-making. The role requires advanced Python or R and SQL skills, expertise in fraud and underwriting models, model deployment, statistical methods, and mentoring junior data scientists.

Build and scale first-line risk control monitoring systems using statistical analysis, automated workflows, and reusable data science modules. Define risk metrics, investigate control performance, establish thresholds and guardrails, and translate analyses into monitoring applications. Partner with risk, compliance, product, and engineering teams to improve governance, identify monitoring gaps, and support decision-making. Lead ambiguous, cross-functional initiatives while communicating findings, limitations, and recommendations to technical and executive stakeholders.

Build and scale first-line risk control performance monitoring for financial products. Develop metrics, key risk indicators, statistical baselines, thresholds, automated workflows, and reusable analytical modules. Partner with risk, compliance, product, and engineering teams to improve governance, identify monitoring gaps, evaluate control effectiveness, and support decisions. Lead ambiguous initiatives from definition through adoption while communicating findings, limitations, and recommendations to technical and executive stakeholders.

The Data Scientist will transform real-world, clinical, and preclinical healthcare data into reproducible datasets, analyses, and evidence supporting medical-device research and regulatory needs. Responsibilities include cohort development, data-quality validation, statistical programming, study design support, analysis plans, clinical and post-market analyses, documentation, and communicating findings to technical and nontechnical stakeholders. The role collaborates with Scientific, Clinical, R&D, Regulatory, Quality, IT, and external data partners.

Analyze neural and behavioral research data end to end, including statistical design, signal quality, preprocessing, quality control, decoding models, pose and kinematic analysis, and reproducible pipelines. The role supports study design, closed-loop sessions, publications, and collaboration with scientific and software teams to translate research methods into reliable workflows.

Designs and leads supply chain data architecture, ELT/ETL pipelines, integrations, dimensional models, data-quality controls, governance, and analytics enablement. The role supports ERP, procurement, inventory, logistics, supplier, and project data while establishing engineering standards and trusted datasets. Responsibilities include dashboards, forecasting foundations, supplier analytics, system implementations, security models, and productionizing data science solutions across capital-intensive AI infrastructure and data center operations.

Serve as the technical lead for data science across Ripple’s product and business teams. Define analytics strategy, create reusable measurement and scientific frameworks, apply AI and agentic workflows to accelerate insight generation, evaluate growth and adoption, communicate executive-level metrics, and mentor data scientists. Work involves experimentation, causal inference, forecasting, statistical modeling, and analysis of payments, stablecoin, custody, and blockchain-related data.

Develop, train, evaluate, deploy, and monitor machine learning and statistical models using Databricks, Python, SQL, Spark, Delta Lake, and MLflow. Prepare datasets, engineer features, manage model lifecycles, validate data, and support governance practices. Translate client requirements into scalable analytics solutions, analyze trends, and communicate findings to technical and non-technical stakeholders while collaborating across data science, engineering, cloud, security, and business teams.

Lead real-world evidence studies for pharmaceutical clients using complex clinical, molecular, claims, EHR, and registry data. Design observational analyses, derive endpoints, apply survival analysis and causal inference methods, interpret results, and communicate findings to technical and non-technical stakeholders. Develop reusable analytical code and standards while incorporating LLMs and agentic workflows into research processes. Collaborate cross-functionally to improve data quality, products, and RWE methodologies, with a focus on oncology and clinical development.

Lead computational platform development for biological design, optimization, and functional prediction. Collaborate with wet-lab scientists to validate predictions, oversee data infrastructure and computational workflows, and contribute to technology roadmaps, funding proposals, and partnerships. The role requires developing machine learning methods for biological sequence and structure analysis, translating biological problems into computational solutions, and leading technical teams across computational and experimental disciplines.

Senior Data Scientist responsible for defining product metrics, designing and evaluating experiments, analyzing growth, retention, and monetization, and building trusted datasets, dbt models, and dashboards. Partners with Product, Engineering, Design, and Marketing to guide roadmap decisions, establish analytical standards, communicate insights to leadership, and mentor teammates.

Leads data science for performance marketing and user growth. Develops marketing measurement, experimentation, causal inference, forecasting, propensity, lifetime value, budgeting, and retention models. Builds dashboards, reporting tools, ETL pipelines, and experimentation frameworks, while defining success metrics and translating complex analyses into strategic recommendations. Partners with Marketing, Product, Engineering, Analytics, and senior leadership to optimize paid marketing investments and business outcomes.

Lead customer-facing data science engagements using production CRM, marketing, firmographic, and intent data. Build, retrain, tune, validate, calibrate, and explain machine learning models for account and contact scoring. Present business impact to nontechnical customers, partner with MLOps to deploy solutions, and convert recurring insights into reusable product capabilities.

Leads the design, development, deployment, and industrialization of causal AI, experimentation, statistical, econometric, and machine learning models. Drives measurement and optimization initiatives, translates business objectives into scalable technical solutions, mentors data scientists, reviews designs and code, and communicates complex causal methodologies to technical and business stakeholders. Requires deep expertise in causal inference, experimentation, measurement, optimization, cloud and big-data environments, and production data science systems.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

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 a small data science team while remaining hands-on in building advertising audience, measurement, ranking, and data-feed models. Set technical direction, apply causal inference and experimental design, build scalable production systems, mentor scientists, and partner with product, engineering, sales, and business development teams. Translate campaign data into trusted measurement products, influence senior leaders and customers, and establish Fetch’s methodology as a market standard.

Own the modeling work behind June’s AI interview evaluation engine. Build, evaluate, and deploy models that score and summarize interviews, develop offline and online evaluation infrastructure, and monitor calibration, drift, and bias. Partner with engineering and product to integrate models into the product and improve them through real-world usage. Help establish data-team tooling and standards while working with LLMs, NLP, ambiguous data, and fairness considerations.