Top Data Science Jobs in San Francisco, CA

Measure and optimize organic growth across SEO, ASO, and content. Build forecasting, attribution, incrementality, and opportunity-sizing models; analyze experiments and platform data; create dashboards; improve data pipelines; and translate findings into recommendations for cross-functional leadership.

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.

Analyze product changes, conduct statistical inference and experimentation, identify growth opportunities, define metrics, build datasets and dashboards, and communicate insights to influence product, engineering, marketing, and business strategy.

Lead and grow Gusto's Sales Analytics team to rebuild sales data infrastructure, advance forecasting and causal-inference capabilities, deliver actionable revenue insights, enable AI-driven self-service analytics, and partner cross-functionally to drive scalable, high-impact analytics for acquisition and expansion.

Data Science PhD interns evaluate modeling and analytics tools, integrate internal and external data, and build data science models for business and customer solutions. The role emphasizes machine learning, deep learning, quantitative analysis, experimentation, and communicating insights to teammates and leadership.

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.

Despite the conflicting description, the posted title is Senior Data Scientist. The body primarily describes an accounting role responsible for reconciliations, journal entries, billing, revenue recognition, financial statements, budgeting, audits, tax schedules, GAAP compliance, and process improvements. The position requires accounting expertise, financial analysis, Excel proficiency, and experience with accounting systems.

Manage accounting operations, including reconciliations, journal entries, billing analysis, expense allocation, revenue recognition, financial statement preparation, budgeting, accruals, audits, and tax schedules. Ensure GAAP compliance, analyze financial data, improve accounting processes, and support management reporting. The role requires strong accounting knowledge, communication, problem-solving, organizational skills, advanced Excel proficiency, and experience with accounting systems.

Manage accounting operations, including reconciliations, journal entries, accounts receivable, accounts payable, billing analysis, expense allocation, revenue recognition, and general ledger maintenance. Prepare financial statements, budgets, variance analyses, tax schedules, and audit materials while ensuring GAAP compliance. Improve accounting processes and communicate financial information across departments.

Customer-embedded principal data scientist who leads account-aligned engagements, translating business needs to production-grade ML/data solutions. Builds and ships end-to-end systems, influences senior stakeholders, accelerates time-to-value, mentors/hand-offs to FDE crews, and navigates complex account dynamics to deliver sustained business impact.

Lead a data science team and personally drive analyses, experiments, and product insights focused on patient experience. Partner with Product, Engineering, Research, and Data Engineering teams to identify consumer pain points, improve product performance, and inform planning. Manage analysts, develop talent, translate technical findings for nontechnical stakeholders, and apply statistical analysis, A/B testing, product metrics, and marketplace analytics to influence business and product decisions.