Top Data Science Jobs in San Francisco Bay Area, CA

Analyze large and complex datasets, build statistical and machine learning models, develop data workflows, enrich datasets, and create analytical solutions for business stakeholders. The role includes writing SQL and Python scripts, optimizing model performance, supporting dashboard development, collaborating with data and BI teams, and documenting models and pipelines. This is a fully remote U.S. position focused on delivering analytics applications through Seek’s cloud platform.

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.

Leads advanced data science and machine learning initiatives for healthcare payment integrity, including fraud, waste, and abuse detection, anomaly identification, predictive modeling, statistical analysis, dashboards, and recovery investigations. Provides technical leadership, mentors analysts, integrates production analytical solutions, collaborates with legal and business stakeholders, and presents findings to leadership. Requires healthcare expertise, advanced SQL and programming skills, and knowledge of claims, billing, reimbursement, and program integrity.

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.

Build Boulevard’s Product Intelligence function by developing product data foundations, pipelines, dashboards, experimentation practices, and customer behavior models. Partner with Product, Engineering, and leadership to deliver actionable insights, self-serve analytics, and productionized models. Establish scalable processes, instrumentation standards, statistical workflows, and data-driven decision-making across the organization.

Develop statistical and machine learning models using soccer tracking and event data to support player recruitment decisions. Build spatio-temporal models, metrics, analyses, and interpretation tools; validate models; communicate findings to professional club clients; and partner with engineering on cloud deployment and production reliability. The role also includes code review, model maintenance, and advancing analytical methods.

Lead end-to-end data science for lending products: define measurement frameworks, analyze experiments and cohorts, evaluate risk and model performance, use AI tools to accelerate analysis, build reusable tooling, and mentor junior scientists while partnering with product, engineering, and risk stakeholders.

Partner with clients to design healthcare data science solutions, curate multimodal clinical datasets, and conduct statistical, predictive, causal inference, real-world evidence, and AI/ML analyses. Responsibilities include querying EMR and other health data sources, creating statistical analysis plans, managing SQL and analytical programming, ensuring HIPAA-compliant data quality, documenting findings, and presenting results to technical, clinical, and executive audiences.

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.

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.

Build and deploy NLP and machine learning models that analyze student and educator text. Develop classical ML and LLM-based features, production inference systems, validation and monitoring pipelines, data-drift detection, retraining workflows, and responsible AI guardrails. Partner with product, engineering, and data science teams to improve real-time education technology features while ensuring accuracy, fairness, privacy, and reliability.

Lead computational analysis for research on intrinsically disordered proteins and regions. Develop reproducible pipelines, integrate HDX-MS, interactomics, cryo-EM, and molecular-dynamics datasets, apply statistical and machine-learning methods, create visualization tools, support experimental design, and collaborate with structural biology, proteomics, and computational modeling teams. Contribute to publications, technical reports, and open computational resources.

Build Way’s data and intelligence layer from the ground up. Own data foundations, metrics, models, experiments, forecasting, recommendations, and decision systems across pricing, merchandising, personalization, and automation. Partner with Product and Engineering to transform ambiguous business problems into rigorous, production-ready solutions, while establishing reliable data products and responsible analytical practices.

Leads responsible AI and model governance while building and deploying healthcare fraud, waste, and abuse models. Establishes standards for documentation, monitoring, bias, drift, validation, and production readiness. Evaluates generative AI use cases, develops policies, validates large-scale claims models, and presents methodology and controls to partners, auditors, and investigators. Works remotely with data scientists, BI developers, and healthcare subject-matter experts.

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.

Develop and productionize machine learning models and predictive analytics using Python, Databricks, PySpark, SQL, and Delta Lake. Build scalable data science workflows, resolve healthcare data quality and entity-resolution issues, and apply Master Data Management practices. Partner with engineering, product, analytics, and business teams to deliver governed, HIPAA-compliant solutions. Perform exploratory analysis, feature engineering, model evaluation, deployment, documentation, and data lineage management for healthcare and member engagement initiatives.

Develop and deploy machine learning models across the full lifecycle, including experimentation, MLOps, monitoring, explainability, and governance. Build scalable model-serving and feature pipelines, contribute to Spark-based data engineering in Databricks and Snowflake, and support orchestration, metadata, security, and compliance practices. Collaborate with platform teams on environment configuration and troubleshooting within HIPAA-governed and FedRAMP-compliant healthcare analytics environments.

Leads and mentors junior developers while designing, modifying, testing, and deploying data solutions in Palantir Foundry. Builds data models and reports from ERP and legacy systems, translates business requirements into technical capabilities, and supports IT modernization. Requires Python, PySpark, cloud architecture, CI/CD, database design, Army domain knowledge, and an active DoD Secret clearance.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.

Develop and lead data science and machine learning initiatives, including exploratory analysis, predictive modeling, experimentation, recommendation systems, user classification, model monitoring, and production deployment. Partner with product, marketing, analytics, and engineering teams to convert user insights into automated services. Manage projects throughout their lifecycle, support strategic ML planning, and deliver solutions for personalization, cross-selling, and retargeting.

Consult as a fractional data scientist for hedge funds, private equity firms, and family offices. Lead data science initiatives: build infrastructure, develop and deploy models, create portfolio dashboards, monitor post-deployment performance, and partner with investment teams to turn data into actionable investment insights. Advise on vendor selection, data quality, technology stacks, KPIs, and governance.

Builds a talent network of experienced data scientists for potential future AI evaluation projects. Responsibilities may include creating grading criteria, evaluating AI-generated or human-produced data science work, writing evidence-based justifications, applying consistent judgment, and incorporating reviewer feedback. Candidates should have professional data science experience, strong Python and SQL skills, expertise in statistical modeling, machine learning, experimentation, causal inference, and excellent technical communication.

Develop marketing analytics and data science models to measure campaign effectiveness, ROI, attribution, customer lifetime value, and marketing impact. Apply causal inference, experimentation, and Marketing Mix Modeling; automate model pipelines and monitoring; build insight-ready datasets and self-service dashboards; and communicate actionable findings to marketing and executive stakeholders. The role also supports campaign analysis, data requirements, training, and strategic planning.

Lead end-to-end pricing experimentation and strategy for consumer and business products. Design and analyze A/B tests, build pricing models and production-grade data pipelines, partner cross-functionally, and present actionable recommendations to senior leadership.

Lead payments-focused data science work: build foundational datasets, metrics, and scalable analytics; analyze customer and transaction behavior (including onchain data); conduct strategic analyses to identify product and revenue opportunities; build reporting, dashboards, and automation; and influence product and business strategy through data storytelling to senior stakeholders.