Top Data Science Jobs in San Francisco Bay Area, CA

Drive product analytics across feature adoption, usage, pricing, customer health, and experimentation. Partner with product, engineering, and business leaders to identify opportunities, deliver actionable insights, improve self-serve analytics and governance, and strengthen data pipelines and analysis quality. The role requires communicating complex findings clearly and working from Aircall’s San Francisco office two days per week.

Manage NASA aerospace portfolio data through cataloging, metadata standards, taxonomies, controlled vocabularies, data lineage, provenance, classification, and quality documentation. Evaluate and implement an enterprise data catalog, integrate it with Dagster, establish metadata and freshness metrics, onboard and retire datasets, and drive adoption across technical and program teams. Translate between data professionals and program stakeholders while improving data discoverability, trust, and usability.

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.

Optimize dataset quality and value through marketplace incentives, task targeting, bonuses, payouts, eligibility, and experimentation. Design treatment assignments and holdouts, evaluate incremental collection against objective functions, identify Goodhart effects, and translate findings into operational actions. Partner with software teams to ship incentive and payout changes into the collection product. Experience with marketplace experimentation, pricing, growth, real-money payouts, or product implementation is preferred.

Lead client engagements from data onboarding through production, plan studies that demonstrate business value, and build automated machine learning workflows using Python, scikit-learn, pandas, and Spark. Translate client business questions into data solutions, manage stakeholder relationships, evaluate model performance, and inform product planning. Requires healthcare or life sciences data experience and end-to-end delivery of sophisticated data or machine learning solutions.

Partner with AI labs to improve frontier models by designing targeted data collection workflows, analyzing experimental results, and implementing quality controls. The role involves evaluating model behavior, reducing bias, supporting model safety and alignment, and translating technical findings into actionable insights for AI researchers and customers. Python is used for data analysis, pipeline development, and workflow prototyping.

Applied Data Scientist focused on incrementality, causal inference, experimentation, Marketing Mix Modeling, and predictive products. The role partners directly with customers to interpret data, diagnose measurement issues, explain statistical results, and provide actionable recommendations. Responsibilities include building diagnostic tools, analyzing model and experiment outcomes, improving methodologies and production capabilities, and collaborating with Product, Engineering, Media Strategy, Customer Success, and senior data scientists.

Supports migration of reporting assets from SAP BusinessObjects to Qlik by rebuilding, testing, validating, documenting, and troubleshooting reports. Responsibilities include creating dashboards, writing SQL queries, updating Qlik scripts and expressions, tracking migration changes, migrating scheduled jobs, and supporting user acceptance testing and training documentation under senior developer guidance.

Own the end-to-end analytical function for a business area such as growth, product, AI and automation, or strategic partnerships. Define metrics, build reports, analyze data, design experiments, generate insights, and influence product and business decisions. Partner with stakeholders to clarify ambiguous problems, communicate findings, recommend actions, and drive measurable outcomes.

Own end-to-end data science for growth, product, AI, automation, or strategic partnerships. Define business questions, metrics, reports, analyses, and experiments; translate findings into recommendations; influence stakeholders and drive measurable outcomes. The role requires strong SQL or Python skills, independent analytical ability, first-principles problem solving, clear communication, and ownership of ambiguous business challenges.

Analyzes structured and unstructured datasets supporting U.S. Navy electronic warfare systems. Develops statistical, machine learning, NLP, data modeling, visualization, and reporting solutions to improve system readiness, reliability, maintainability, operational availability, and lifecycle performance. Integrates logistics, engineering, maintenance, and operational data; identifies trends and risks; and presents data-driven findings to technical and program stakeholders.

Develops analytical models and applications for U.S. Navy electronic warfare and sustainment systems. Applies data mining, statistical analysis, NLP, machine learning, and data modeling to structured and unstructured data. Integrates logistics, engineering, maintenance, reliability, and operational data to assess readiness, performance, risks, and availability. Creates visualizations, dashboards, reports, and briefings, while collaborating with engineers, logisticians, operations researchers, and program personnel to support lifecycle management and data-driven decisions.

Leads development of statistical and machine learning models for time-series, geospatial, kinematic, and radar data. Responsibilities include feature engineering, forecasting, anomaly detection, data integration, ETL, visualization, algorithm evaluation, simulation, dataset creation, and stakeholder communication. The role also contributes to mission-focused software applications, APIs, data pipelines, documentation, code reviews, and agile development while requiring an active Top Secret or CBP/DHS suitability clearance.

Develops data science models, analytical processes, visualizations, dashboards, and reports for U.S. Navy electronic warfare systems. Integrates logistics, engineering, maintenance, reliability, and operational data to evaluate system readiness, reliability, maintainability, performance, risks, and lifecycle sustainment. Applies data mining, statistical analysis, NLP, and machine learning to support operational decisions and collaborates with engineers, logisticians, analysts, and program personnel.

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.

Set the technical vision for data science, lead complex statistical and machine learning model development from concept through production, and architect scalable modeling platforms and pipelines. Develop credit risk, pricing, and loss forecasting models; establish model governance standards; partner with senior leaders; mentor data scientists; and communicate technical strategy to executives. The role emphasizes production deployment, monitoring, cloud architecture, and high-impact decision-making across the business.

Develop, deploy, and monitor predictive models supporting Basketball Operations decision-making. Build model features, analyze large datasets, identify trends, and collaborate with data engineering and analysts to integrate results into organizational decisions. The role requires Python, predictive modeling, SQL, cloud, and production analytics expertise, along with strong communication and presentation skills. This is a full-time remote position with a flexible schedule that includes nights, weekends, and holidays.

Lead end-to-end data science for Cash App lending products: define measurement frameworks, run experiments, evaluate model and policy performance, detect bias/drift, build decision support, and mentor junior data scientists while partnering with product, engineering, and risk.

Analyze large customer datasets, evaluate machine-learning models, develop risk and fraud insights, and communicate data-driven recommendations to technical and nontechnical audiences. The role partners with sales and account teams throughout pre- and post-sales, creates automated analytical workflows, supports product optimization and upsell opportunities, and may build exploratory ML models. It also contributes thought leadership on identity verification, fraud prevention, model performance, and customer risk management.

Own Alpaca’s growth data strategy across acquisition, activation, retention, referral, and monetization. Build experimentation frameworks, causal and incrementality analyses, funnel and cohort models, LTV/CAC and churn forecasts, marketing measurement, governed growth metrics, and self-serve analytics. Partner with Product, Marketing, Sales, Engineering, and Finance to influence strategy and roadmaps, communicate insights to leadership, mentor data professionals, and establish standards for growth analytics.

Analyzes financial and ERP data using statistical analysis, data mining, predictive modeling, and advanced algorithms. Supports Oracle financial shared services, data conversion, business continuity, disaster recovery, and contingency planning. Develops analytical outputs that improve agency KPIs and identifies trends and business opportunities. Requires collaboration with systems, network, hosting, and disaster recovery teams, plus participation in COOP exercises and Oracle environment upgrades.

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.