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

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.

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.

Conduct media, social, and policy research to identify reputational issues, risks, and narratives. Prepare alerts, briefings, reports, presentations, and recommendations for clients. Support monitoring, benchmarking, communications measurement, data organization, quality control, and analysis using Excel and research tools. Collaborate with account teams, participate in client meetings, manage deadlines, incorporate feedback, and help improve research quality.

Build and own Shepherd’s canonical analytical layer using dimensional modeling and dbt. Partner with actuarial, reporting, operations, engineering, and other data consumers to define trusted business entities, facts, dimensions, relationships, and rules. Establish testing, reconciliation, documentation, lineage, and adoption practices; evaluate and migrate existing models and datasets; and shape semantic context for human and AI-assisted analytics.

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.

Build and scale foundational data infrastructure for AI-powered hardware and consumer devices. Responsibilities include developing reliable pipelines, integrating manufacturing, telemetry, quality, and customer operations data, creating durable data models, enabling manufacturing and product intelligence, and establishing data engineering standards. The role requires strong architectural judgment, distributed systems knowledge, production infrastructure experience, and cross-functional collaboration in an early-stage organization.

Conducts exploratory and defined analyses of criminal justice datasets, identifies trends and discrepancies, prepares analytical data for state product launches, and builds documented visualizations and dashboards. The analyst communicates findings to internal teams and state partners while collaborating with senior analysts and engagement teams. Responsibilities include cleaning real-world data, supporting product launches, evaluating product impact, and developing expertise in assigned states’ data, policies, and products.

Own and refine financial and profitability metrics, build curated data models, dashboards, and analytical views, and analyze large-scale financial data. Partner with Finance, Product, Operations, and Data teams to support planning, capital allocation, KPI development, reconciliation, and data quality. Design GenAI-powered self-service analytics tools and communicate insights, assumptions, and recommendations to technical and senior business stakeholders.

Build modern analytics infrastructure, self-service AI analytics tools, foundational datasets, and business data pipelines. Partner with teams to track product metrics, support financial and marketing reporting, identify cost savings, and improve business decisions through strategic analysis and consulting.

Owns dataset strategy and quality for physical labor AI data. Defines the ideal dataset, objective functions, taxonomy, golden sets, acceptance criteria, audits, and incremental data value. Converts these frameworks into collection strategies, operational standards, and vendor requirements while optimizing information gain over unnecessary fidelity or volume. Partners with marketplace, software, research, and evaluation teams to connect dataset quality with model capability.