Top Entry Level Data & Analytics Jobs in San Francisco Bay Area, CA

Early Career Analyst supporting Affirm’s Risk & Analytics organization through data analysis, portfolio monitoring, reporting, risk and growth strategy insights, and cross-functional collaboration. The role uses SQL and Python to build scalable reports, support machine learning and risk model deployment, assess lending regulations, and improve product launches and market expansion. Analysts will integrate AI tools into workflows while validating outputs and developing expertise in consumer lending and quantitative business decision-making.

Build and maintain scalable data pipelines, analytical data models, backend data systems, business rules, metrics, and data sources. Partner with teams across finance, growth, product, customer support, sales, and marketing to support analytical needs and improve data-driven decision-making. Own data engineering problems end to end and help advance organizational data practices.

Supports digital assurance and technology projects by analyzing data, evaluating internal controls, improving financial reporting processes, and contributing to regulatory compliance efforts. The Associate uses Python and Microsoft SQL Server Data Tools to generate insights, supports auditors and client engagements, conducts research, and collaborates with teams to deliver transparent, high-quality recommendations. This entry-level role is designed for candidates completing a relevant bachelor’s degree and developing auditing, data analysis, communication, and client-service skills.

Supports credit underwriting and pricing strategies for lending products. Responsibilities include designing A/B tests, analyzing loan performance, building KPI dashboards, identifying risk and revenue opportunities, evaluating alternative data sources, recommending mitigation strategies, and partnering with technology teams to implement strategies accurately.

Reviews annotated sensor and map data for quality, identifies recurring issues, and recommends solutions. The role prepares reports, maintains annotation policies, develops training documentation, performs data annotation, tests internal tools, reports bugs, and creates visual assets. It also collaborates with stakeholders, partners, annotators, project managers, and engineers while supporting on-road operations and evolving program priorities.

Analyze Deploy’s media and sports offerings, priorities, metrics, client solutions, and commercialization strategies. Support multiple projects, communicate analytical findings, collaborate with machine learning and engineering teams, and contribute to business growth and client outcomes. The role also involves monitoring industry practices, protecting information assets, and supporting awareness of Deploy’s AI-powered products among target media, sports, agency, and advertiser communities.

Lead and scale Ticketmaster’s product experimentation program by establishing governance, standards, onboarding, and measurement practices. Configure and monitor Optimizely experiments, design statistically rigorous A/B and multivariate tests, analyze results, and translate findings into product recommendations. Define digital metrics, identify fan-experience opportunities, build self-service analytics resources, and partner with Product, Design, Engineering, Research, and Analytics teams to support data-informed decisions.

Research AI infrastructure and economics across GPU cloud providers, AI model developers, and AI tokenomics. Build company datasets, reconcile conflicting evidence, maintain financial and unit-economics models, analyze disclosures and technical materials, conduct interview research, draft research notes and charts, and support consulting projects with market and financial analysis.

Build and maintain Nexus, an enterprise AI implementation platform. Develop backend services, APIs, integrations, orchestration workflows, automated data pipelines, transformations, semantic models, validation processes, and governed lakehouse assets. Partner with AI engineers and enterprise customers to deploy solutions, resolve complex data and system requirements, and create reusable integrations. Ensure secure, monitored, recoverable, and repeatable deployments across customer environments.

Designs, builds, secures, and operates scalable databases and AWS database infrastructure. Responsibilities include schema design, SQL and query optimization, performance monitoring, database security, backup and disaster recovery, capacity planning, incident troubleshooting, documentation, and infrastructure automation. The role partners with application and data teams and supports database environments from development through production.