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

Build and scale production Python pipelines for economic and financial climate-risk models. Responsibilities include transforming research prototypes into tested infrastructure, supporting statistical, econometric, and machine-learning modeling, cleaning large financial and hazard datasets, resolving data anomalies, and collaborating with engineering, product, and client teams to document outputs and communicate findings.

The role involves structuring and ingesting customer data, setting up ETL pipelines, and providing solutions for data export and import.

Cellanome is looking for passionate individuals to join their start-up team aimed at tackling significant challenges in biology. They offer diverse roles, emphasizing collaboration, innovative spirit, and career growth opportunities.

The Data Engineer will build and scale Baseten's internal data platform, designing data models and pipelines while enabling self-service analytics and ensuring data reliability.

Develop ETL and data visualization solutions for a big data customer and partner experience transformation program. Build native Hadoop and Vertica ETL pipelines, collaborate with stakeholders, and support data integration and visualization deliverables for analytics teams.

Datastage Developer role in banking. Responsible for developing, maintaining, and troubleshooting DataStage ETL jobs to support data integration and reporting. Collaborate with stakeholders and teammates to ensure reliable data delivery and performance.

DiffUSE seeks individuals interested in interdisciplinary roles involving computational biology, machine learning, and open scientific tooling. Responsibilities include data processing, ML research, infrastructure engineering, and community building.

This position involves supporting client-based data analytics and engineering projects, utilizing SQL, Python, or R, and presenting findings clearly.

Develop and maintain ServiceNow reports, dashboards, performance analytics, data extracts, and Power BI executive reporting. Build data models, KPIs, measures, and visualizations for operational performance, compliance, service health, and engineering effectiveness. Automate reporting, data collection, validation, distribution, and notifications using Power Automate, PowerShell, Python, or similar tools. Support Workspace ONE, Intune, macOS troubleshooting, monitoring, endpoint compliance, incident resolution, documentation, and change management.

Analyze banking and financial crime data, develop and tune statistical and machine learning classification models, evaluate model performance, identify data quality issues, and recommend optimization strategies. The role uses Python and SQL for feature engineering, statistical modeling, automation, and large-scale data processing. Responsibilities also include documenting methodologies, reporting results to management and regulators, and collaborating with compliance, technology, business, and model governance teams.