Top Data Science Jobs in San Francisco, CA

Leads paid marketing analytics strategy, including attribution, experimentation, incrementality, marketing mix modeling, offer measurement, and budget allocation. Partners with Marketing, Finance, Data Engineering, and Data Science teams to turn analysis into executive decisions. Defines measurement frameworks, improves data models and pipelines, uses AI tools to enhance analytics, forecasts marketing impact, and mentors senior data scientists and contractors.

Leads an enterprise data science team developing agentic AI products and machine learning solutions. Responsibilities include defining AI strategy, designing experiments and models, evaluating and validating modeling systems, analyzing telemetry, building benchmark and challenger models, and advising senior business and regulatory stakeholders. The role requires extensive experience with large-scale data analysis, machine learning, relational databases, open-source programming languages, cloud platforms, software architecture, and DevOps practices.

Own experimentation, statistical analysis, causal inference, metrics, and risk modeling to support strategic product and business decisions. Translate ambiguous questions into actionable analyses, communicate data-driven recommendations, and collaborate with Risk, Engineering, Product, Design, Operations, Identity, and Compliance teams. The role requires strong SQL and Python skills, leadership of technically complex projects, stakeholder influence, mentoring, and use of AI-assisted analytics.

The Data Science Intern will analyze data, build analytical models and datasets, support A/B tests and causal inference experiments, and deliver actionable recommendations to product and engineering teams. The role involves maintaining data pipelines, conducting analytical deep dives, and communicating insights to technical and non-technical audiences. This is a 12-week summer internship with mentorship from senior data scientists and exposure to data-driven decision-making in the crypto industry.

Lead end-to-end direct mail marketing analytics and campaign execution for lending and deposit products. Manage campaign logic, data pipelines, validation, and performance measurement. Partner with marketing, channel owners, and external vendors to optimize targeting, run experiments, and report ROI. Oversee offshore team execution, translate business needs into technical solutions, and present insights to senior management.

Lead end-to-end direct mail campaign analytics and execution for financial products: define targeting and suppression rules, build and validate data pipelines, manage offshore execution, measure ROI, partner with marketing and vendors, and translate results for senior leadership.

Leads a multidisciplinary data science and data engineering team supporting Finance. Sets strategy, manages priorities, guides analytical and modeling work, improves trusted data foundations, advances AI and machine-learning capabilities, and establishes scalable operating mechanisms. Partners with Finance, Engineering, Data, Product, and executives to deliver reliable insights, forecasting, automation, and measurable business impact while developing team talent.

Leads GitLab’s Product, Sales, and Finance Data Science teams, directing strategy, prioritization, executive partnership, and business-impact programs. Manages managers and senior individual contributors, develops organizational standards for experimentation, causal inference, predictive modeling, and AI, and builds a high-performing data science culture. The role connects analytics and data science to product adoption, revenue growth, consumption, and executive decision-making while partnering with data and analytics engineering teams.

Leads product analytics strategy across Xbox Console, PC, Store, and Platform experiences. Establishes metrics and measurement frameworks, identifies growth opportunities through advanced analytics and machine learning, directs experimentation and causal inference, builds reusable analytical and AI-enabled capabilities, improves telemetry and measurement infrastructure, and mentors data scientists while influencing senior leaders and portfolio investment decisions.

Sets the technical vision for data science, leads complex statistical and machine learning initiatives from concept through production, and architects scalable modeling ecosystems for credit risk, pricing, and loss forecasting. The role partners with senior leaders, establishes model development standards, builds monitoring frameworks, mentors technical talent, and communicates strategy to executives. Key responsibilities include production deployment, model validation, roadmap development, and delivering measurable business impact across financial services.