Top Data Science Jobs in San Francisco Bay Area, CA

Leads the design, development, and deployment of scalable AI, generative AI, and machine learning solutions. Oversees data infrastructure, integration, data quality, and advanced analytics while managing client engagements and executive relationships. Drives business development, strategic direction, innovation, and AI-driven automation. Mentors teams, develops future leaders, and fosters collaboration, integrity, and client satisfaction across complex projects.

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.

Build trusted financial and business metrics, analytical datasets, dashboards, and recurring reports. Investigate performance drivers and variances, translate ambiguous questions into analyses and recommendations, support forecasting and planning, improve data quality with Data Engineering, and communicate findings to Finance and business stakeholders.

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.

Turn business data into decisions by identifying high-impact problems, defining company-wide metrics, conducting A/B experiments, building reliable ETL pipelines and data models, and delivering analysis, forecasting, dashboards, and modeling. Partner cross-functionally with product, engineering, research, operations, and go-to-market teams to shape priorities and ensure trustworthy, well-documented data.

The Data Scientist will lead data science initiatives embedded in Marketing, analyzing campaigns, conversion paths, incrementality, media mix, segmentation, experimentation, and revenue modeling. Responsibilities include translating marketing problems into analytical projects, conducting rigorous mathematical and statistical analyses, collaborating on strategic initiatives, managing project execution, communicating technical findings, and mentoring junior data scientists. The role requires strong Python, SQL, causal inference, mathematical, software engineering, and communication skills.

Develops fraud detection models using client data, including data cleaning, feature engineering, model tuning, and scalable pipeline development. Collaborates with clients and internal product and research teams, communicates technical findings to diverse audiences, guides project execution, and contributes to generalized product enhancements and fraud prevention solutions.

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.

Develop and evaluate machine learning models for fraud detection across account opening, account takeover, and identity risk. Responsibilities include exploring large datasets, identifying fraud patterns, engineering features, testing models, writing production-quality Python code, supporting model monitoring, and communicating analytical findings. The role also applies data privacy, explainability, validation, documentation, and governance standards while collaborating with senior data scientists and engineering teams.

Develop advanced statistical and machine-learning solutions for fraud and other data-intensive business problems. Translate ambiguous needs into hypotheses, experiments, models, and prototypes; shape data strategies; evaluate emerging generative and agentic AI technologies; and partner with Product, Engineering, Analytics, and business teams to operationalize solutions. The role also includes mentoring junior team members and fostering experimentation and innovation.

Define product, feature, and agent metrics; design and analyze experiments; evaluate model and product performance; investigate anomalies and regressions; build statistical models, dashboards, and analytical tools; and translate complex analyses into recommendations for Product, Engineering, and Machine Learning teams. The role focuses on connecting offline AI evaluations with production outcomes and customer impact.

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.

Supports data analysis, predictive model development and monitoring, partner oversight, data quality investigations, and analytical solution documentation. Uses Python, SQL, cloud and visualization tools to analyze complex datasets, automate monitoring and validation workflows, identify root causes, and communicate insights to technical and business stakeholders. Collaborates with risk, compliance, technology, operations, vendors, and external partners in a regulated financial services environment.

Build and deploy machine learning models for credit and fraud risk, using large-scale financial data to improve lending decisions and business performance. Monitor production models, validate algorithms and features, conduct risk analyses, follow regulatory and responsible AI practices, and collaborate with engineers and cross-functional stakeholders. Develop productivity tools, document models, and communicate technical findings to senior management.

Leads data science initiatives by analyzing complex structured and unstructured datasets, developing machine learning algorithms and systems, and delivering analytical products and dashboards. Works with clients and stakeholders to define needs, communicate development plans, brief senior leadership, and provide data-driven recommendations. Mentors team members, identifies opportunities for data science solutions, and manages competing priorities in complex environments.

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.

Develop and deploy data science solutions for pharmacy benefits and member experience, including predictive models, generative AI pipelines, and rules-based logic. Extract and analyze large-scale data with Python and SQL, manage model lifecycles, monitor production performance, and communicate insights to clinical and business leaders. Collaborate with data scientists, engineers, and operational stakeholders to solve complex healthcare and PBM problems.

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

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.

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 real-world evidence studies using complex clinical and molecular data, observational study design, survival analysis, causal inference, and machine learning. Collaborate with pharmaceutical partners, derive clinical endpoints, interpret results, develop reusable analytical code, and apply LLM-based tools to accelerate workflows. Communicate findings to technical and non-technical stakeholders through reports, presentations, manuscripts, and conference materials while partnering cross-functionally to improve data quality and analytical practices.

Conduct workforce analytics and applied research on attrition, engagement, mobility, performance, and planning. Perform statistical analyses, regression modeling, data extraction and validation using SQL and R or Python. Translate HR business questions into actionable insights, communicate findings to stakeholders, and protect sensitive workforce data.

Commercial Data Scientist will develop machine learning models, analyze large healthcare datasets, identify clinician behavioral patterns, and create reusable data products. The role partners with Product, Data, Strategy, Insights, and Sales teams to deliver client-facing analyses and data-driven recommendations. Responsibilities include statistical analysis, production model development, complex SQL querying, scalable data processing, automation, and communicating technical insights through compelling visualizations and narratives.

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.