Top Data Science Jobs in San Francisco Bay Area, CA

Leads data science for Anthropic’s Core Apps product organization, partnering with product leaders on strategy, roadmaps, investment decisions, and success metrics. Manages and develops a team of Product Data Scientists, establishes experimentation and causal inference standards, leads high-impact analyses, communicates recommendations to executives, and builds foundational analytics practices and infrastructure. Requires extensive product data science experience, people management, strong statistical expertise, and fluency in Python and SQL.

Leads forward-deployed data science engagements for strategic customers, tailoring predictive models to customer data and go-to-market processes. The role owns discovery, production deployment, validation, model changes, customer reviews, and measurable business outcomes. It also productizes recurring solutions, influences the predictive roadmap, establishes team standards, and mentors data scientists. Strong production machine learning, Python, SQL, customer-facing consulting, communication, and product judgment are required.

Own the end-to-end data science lifecycle for workforce identity and hiring fraud detection. Analyze identity, device, behavioral, resume, and application data; develop fraud rules, machine learning models, and anomaly detection systems; and support deployment and monitoring in RiskOS. Collaborate on GenAI resume verification and explanation tools, evaluate LLM components, partner with engineering on productionization, and communicate model performance and customer impact to product, GTM, and consulting stakeholders.

Develop personalization and machine learning solutions across product, growth, and monetization. Own projects from opportunity discovery and data preparation through modeling, experimentation, product integration, launch evaluation, and iteration. Build production scoring workflows, translate predictions into product actions, apply causal reasoning, and collaborate with engineering and product teams. Projects include recommendations, promotion targeting, retention, customer lifetime value, and lifecycle management.

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.

Develop and validate machine learning and predictive models, perform statistical and data analysis, conduct ETL across repositories, and assess model quality. Communate findings through visualizations and written reports, automate analytical workflows, and collaborate directly with nontechnical customers to refine requirements and deliver data-driven solutions. The role requires expertise in ML/AI, programming, cloud environments, CI/CD, and government or safety-critical systems.

Drive revenue growth and advertiser value through data science for Nextdoor’s Ads Marketplace. Responsibilities include developing ranking and optimization models, designing experiments, analyzing auction performance, building explainability feedback loops, and delivering strategic insights. The role partners with Product, Engineering, Marketing, Sales, Finance, and Operations to improve advertising products and explore AI and large language model applications.

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.

Own the product modeling roadmap and end-to-end lifecycle of production machine learning models, including recommenders, routing, and agent assignment. Engineer features, design experiments, deploy iterative improvements, and define real-time model integration with engineering and product teams. Apply Bayesian methods, causal inference, and marketing measurement while translating uncertainty into business recommendations. Mentor teammates and raise data science standards through code review and documentation.

Experienced Data Scientist supporting federal data-driven initiatives through statistical analysis, predictive modeling, machine learning, data preparation, visualization, dashboards, and actionable insights. The role collaborates with technical, business, and program stakeholders, advises on analytical methodologies, improves data quality, and contributes to modernization, automation, and emerging technology efforts.

Develop and deploy statistical and machine learning models, conduct exploratory analysis, forecasting, segmentation, experimentation, and AI initiatives. Translate business needs into analytical frameworks, KPIs, predictive insights, and recommendations. Collaborate with data engineering, BI, QA, and business teams to ensure data readiness, model integration, quality, governance, and auditability. Support revenue, marketing, financial, and operational decision-making through scalable analytical solutions.

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.

Create reproducible data science and machine learning tasks with reference solutions, robust tests, and evaluation criteria. Investigate coding-agent failures, identify errors, data leakage, and flawed assumptions, then refine tasks accordingly. Document requirements, solution reasoning, and verification steps. The role requires competitive data science experience, mathematical reasoning, programming ability, and practical knowledge of machine learning and quantitative modeling.

Analyze product and operational data to guide roadmap decisions, define metrics, evaluate experiments, uncover user friction, and build reliable dbt models, dashboards, and analytical tools. The role partners closely with Product and Operations, improves data quality and instrumentation, and translates complex findings into actionable recommendations. It requires strong SQL, Python, experimentation, data modeling, visualization, and product judgment skills.

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.

Develop pricing and rider-engagement algorithms, including incentive, budget-management, and real-time optimization systems. Build and deploy statistical, machine-learning, and optimization models in production using Python. Design experiments, establish product metrics, analyze marketplace outcomes, and identify business opportunities. Partner with Product, Engineering, Analytics, business teams, and executives to frame problems, guide roadmaps, and communicate data-driven recommendations.

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.

Designs, builds, evaluates, and deploys machine learning, LLM, and agentic AI capabilities for cybersecurity detection, investigation, and response. Analyzes complex security data, develops models and evaluation frameworks, creates RAG and retrieval workflows, and partners with engineering teams to productionize and monitor AI systems. The role requires addressing adversarial, noisy data and balancing accuracy, explainability, latency, reliability, and operational impact.

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.

Design, deploy, and optimize production generative AI agents and agentic workflows for enterprise applications. Responsibilities include tool schemas, APIs, orchestration, RAG, memory, evaluation, observability, guardrails, security, and human-in-the-loop escalation. The role partners with engineers and business product owners to integrate reliable GenAI systems across regulated commercial, medical, and government affairs environments.

Develop and evaluate foundational deep learning methods, including transformers, embeddings, and neural networks, for credit modeling and loss prediction. Integrate models into ML pipelines, apply representation learning to financial data, and build specialized AI agents for data science and business workflows. Collaborate with technical and product teams, communicate findings, mentor colleagues, and measure model impact on customer and business outcomes.

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.

Partner with Slack’s GTM organization to use advanced analytics, experimentation, statistics, and predictive modeling to improve growth, customer outcomes, sales performance, and strategic decision-making. Build scalable analytical frameworks, forecasting methods, measurement strategies, and intelligence solutions. Collaborate with Data Engineering, Product, Sales Strategy, Finance, and executives to develop trusted datasets, communicate insights, and influence GTM strategy.

Develop and refine credit risk strategies, rules, monitoring tools, and performance dashboards. Use SQL, Python, statistics, and data science to analyze complex datasets, identify risk trends, optimize processes, and influence business decisions. Collaborate with product, engineering, and operations teams to implement strategies across the customer lifecycle, while mentoring junior team members.