Top Analysis Reporting Jobs in Milwaukee, WI

As a Senior Data Scientist, you will develop statistical models and machine learning algorithms to improve patient enrollment and trial management while ensuring compliance in a healthcare data environment.

The job involves leveraging AWS and Snowflake for data engineering tasks within a technology-focused company, requiring US residency.

As a Data Science Intern, you'll learn to translate business challenges into statistical problems, analyze large datasets, build machine learning models, and communicate insights effectively.

Synthesize analyses across product, marketing, reporting, and data science to create executive-ready narratives and decision materials. Partner with analytics teams and business leaders to surface high-impact insights, improve discoverability and adoption of analytics, set standards for storytelling, and ensure insights drive organizational alignment and action.

Design, build, and maintain automation workflows and data pipelines across admissions, education, career services, and billing. Create BI dashboards, ensure data accuracy, manage lightweight databases (e.g., Supabase), and support cross-functional teams to improve operational efficiency and scalability.

Lead analytics for member engagement and revenue recognition: build and validate Braze attributes, run attribution and A/B test analyses, maintain Looker dashboards, join campaign and engagement data via SQL/DBT, and provide actionable recommendations to Member Operations and Marketing.

Partner with stakeholders to gather requirements, validate integrated datasets, and deliver trusted insights via SQL-driven analysis and Tableau/Qlik dashboards to support federal healthcare operational decisions.

Design, implement, and maintain dbt/Snowflake ELT transformations to convert raw clinical and multimodal health data into trusted, analysis-ready models; enforce data quality, lineage, testing, CI/CD, and documentation; collaborate with clinical and ML teams to harmonize ontologies and support de-identification and ETL across health systems.

Analyze, clean, and validate data from multiple sources; develop and run SQL queries; calculate KPIs and financial metrics; create visualizations and present insights to non-technical stakeholders; collaborate with business partners to translate requirements into data-driven recommendations.

Lead and grow a data engineering team, set technical standards and roadmap, architect scalable GCP data solutions (ingest, transform, warehouse, serving), ensure pipeline reliability and data quality, partner with analytics and client teams, drive best practices (CI/CD, IaC, governance), and evaluate new tooling.