Top Data Science Jobs in San Francisco Bay Area, CA

Owns credit policy development and governance for Square Financial Services, including underwriting strategy, policy experimentation, portfolio monitoring, regulatory documentation, and risk reporting. Designs rigorous champion-challenger tests, randomized holdouts, and staged rollouts while balancing growth, losses, profitability, fair lending, and bank oversight requirements. Partners with Risk, Compliance, Internal Audit, and senior leadership to scale credit products responsibly.

Analyze large datasets using quantitative methods, data mining, statistical modeling, and AI tools to generate actionable product and business insights. The role designs and tracks product metrics, builds dashboards and reports, identifies trends and opportunities, and supports data-driven decisions. Responsibilities include collaborating with product managers, engineers, marketers, and designers, applying statistical techniques such as A/B testing and causal analysis, and independently driving projects to completion.

Develop and lead complex AI and data science solutions, shape AI technical strategy, research emerging technologies, resolve large-scale technical problems, and guide cross-functional teams. The role focuses on scalable data solutions, machine learning, deep learning, large language models, generative AI, NLP, computer vision, Python, and Vertex AI. It requires a bachelor's degree and at least five years of progressive data analytics and data science experience.

Lead data science for SEO and AEO-driven patient growth. Develop measurement and experimentation strategies, combine internal and external data sources, evaluate causal impact, answer product and business questions, and automate analysis using AI agents and workflows. Partner with product, marketing, engineering, content, and design to improve organic search and AI visibility. Establish measurement standards and communicate insights, limitations, and recommendations to executives and cross-functional teams.

Analyze acquisition data and guide marketing strategy, budget allocation, channel optimization, and growth decisions. Design incrementality experiments, develop marketing mix and allocation models, define acquisition metrics, identify new growth opportunities, and improve web conversion. Partner with Marketing, Engineering, Finance, Design, and User Research to translate statistical and machine learning insights into measurable business outcomes across paid, organic, consumer, and enterprise channels.

Embedded Data Scientist for Superhuman Mail, partnering with product, growth, engineering, and design teams to guide roadmap decisions. Responsibilities include defining product metrics, designing experiments, applying causal inference and machine learning, measuring AI feature quality, analyzing activation and retention, identifying growth and conversion drivers, and communicating insights to stakeholders. The role requires strong Python, SQL, experimentation, statistics, and cross-functional influence skills.

The Data Scientist will partner with Sales, Revenue Operations, and Finance to analyze GTM performance, build and productionize machine learning models, conduct experiments, forecast outcomes, and identify growth opportunities. Responsibilities include statistical analysis, causal inference, Python and SQL development, MLOps, agentic workflow automation, semantic-layer contributions, executive reporting, and communicating insights to nontechnical stakeholders in a fully remote environment.

Build and evaluate fraud models for onboarding, login, account changes, and money movement. Develop labels, features, sampling strategies, thresholds, and production monitoring to detect account takeover, scams, money mules, and identity fraud. Transition third-party risk models to in-house solutions, measure model performance and drift, collaborate with fraud, risk, operations, and compliance teams, and evaluate AI agent workflows for transaction review and fraud-ring analysis.

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

Owns end-to-end credit risk, underwriting, fraud detection, collections, and loss forecasting models. Leads model development, validation, backtesting, monitoring, and production deployment while partnering with Risk, Product, Engineering, Finance, Compliance, and Capital Markets. Serves as a risk modeling subject matter expert, mentors data scientists, supports model governance and fair lending analysis, and communicates findings to senior leadership.

Build and optimize machine learning models for large-scale, real-time digital performance analytics. Develop predictive modeling, anomaly detection, time-series analysis, automated alerting, and deep learning solutions. Process data with Ray and Spark, prototype using AI coding tools, and integrate ML solutions into production with engineering and product teams. The role also contributes to AI initiatives, roadmaps, and scalable intelligent platform capabilities.

Conduct statistical studies and analyze complex multimodal clinical datasets, including clinical notes, neuropsychological assessments, and neuroimaging. Build and maintain data pipelines, databases, and data organization systems; develop and evaluate machine learning and generative AI models; identify trends and multivariate relationships; prepare reports and presentations; support data security practices; and contribute to research publications and multidisciplinary health AI initiatives.

Conduct statistical analysis, causal inference, experimentation, predictive modeling, and machine learning using large datasets. Build data pipelines, dashboards, reports, and production models with Python or R, SQL, and Spark. Analyze fraud, financial crime, and business outcomes, communicate actionable recommendations, collaborate with multidisciplinary teams, and present findings to stakeholders.

Develop end-to-end machine learning solutions, including data exploration, feature engineering, algorithm development, validation, and production deployment. Analyze terabyte-scale datasets, write production-quality code, create data science pipelines, and communicate findings to technical and non-technical stakeholders. Collaborate with Product and Engineering teams on fraud, payment, and financial risk modeling, while supporting model performance, workflow automation, and data confidentiality.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; surface insights; and drive recommendations and decisions with cross-functional stakeholders. The role requires independent analytical problem-solving, strong communication, and ownership of ambiguous initiatives that materially improve business outcomes.

Own end-to-end data science for a business area such as growth, product, AI, or strategic partnerships. Define business questions, metrics, reports, analyses, and experiments; generate actionable insights; and influence product, growth, finance, and operations decisions. The role requires independently structuring ambiguous problems, using SQL or Python, communicating findings clearly, and driving measurable business outcomes.

Own end-to-end data science for growth, product, automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; and influence product, growth, finance, and operations decisions. The role requires strong SQL or Python skills, independent analytical problem-solving, clear communication, and ownership of ambiguous initiatives.

Leads high-impact data science initiatives across marketing, growth, and product. Designs experiments, causal measurement frameworks, incrementality models, uplift and heterogeneous treatment effect models, and personalization and optimization solutions. Partners with cross-functional leaders to improve acquisition, retention, ROI, conversion, and engagement. Defines technical strategy, promotes scalable workflows and AI-assisted development, communicates recommendations to executives, and mentors data scientists and analysts.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; communicate insights and recommendations; and drive measurable outcomes with cross-functional stakeholders. The role requires strong analytical thinking, independent data work using SQL or Python, ownership of ambiguous problems, and persuasive communication with leadership.

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

Staff Data Scientist partnering with Growth, Marketing, Product, and Engineering teams to build data science solutions that drive customer acquisition, engagement, and retention. Responsibilities include predictive modeling, personalization, recommendation systems, LLM applications, incentive optimization, causal inference, marketing impact measurement, technical mentorship, and strategic leadership. The role requires deploying production machine learning systems and communicating analytical insights to senior stakeholders.

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.

Evaluate and improve AI mitigation systems, classifiers, and detection pipelines across high-risk domains. Build monitoring and measurement frameworks, analyze false positives and negatives, identify adversarial trends, quantify customer impact, and recommend prioritized interventions. The role requires rigorous experimentation, causal analysis, dashboarding, and cross-functional collaboration with engineering, product, research, and executive stakeholders to improve AI safety and preparedness.

Data Scientist Intern will analyze large-scale datasets, design A/B experiments, improve experimentation and analytics tooling, and translate insights into product recommendations. The role involves collaboration with Data Science, Product, and Engineering teams, use of SQL and Python, statistical analysis, AI-assisted workflows, and presenting findings to leadership. This hybrid internship runs June 1 through August 31, 2027, with three in-office days weekly in San Francisco.

Own the reliability and quality of an AI copilot for trading platforms. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement loops. Partner with engineering and product on safe tool interactions, auditability, and user trust. Develop trading-domain expertise and measurement systems covering correctness, safety, latency, regression risk, and workflow performance.