Top Senior Analysis Reporting Jobs in Milwaukee, WI

This role transforms workforce and HR data into visual narratives for executives, developing reusable templates and collaborating with leaders. Responsibilities include creating insights decks, infographics, and ensuring visual clarity and quality across all workforce narratives.

The Sr. Data Analyst will deliver actionable insights to stakeholders, develop predictive models, and support data-driven growth by analyzing diverse datasets.

The Market Risk Analyst will monitor SBL lines of credit, assess market risks, manage collateral, develop risk models, and prepare reports.

The Senior Product Analyst at Toast collaborates with various teams to analyze data, develop measurement frameworks, and enhance product development through insights, dashboards, and reporting tools.

Lead architect for Compliance data at Coinbase, defining data governance frameworks, designing data contracts, mapping data lineage, and quantifying data impact to enhance compliance reporting and data quality processes.

As a Senior Analyst, you'll gather and analyze requirements, implement process improvements, manage financial reporting in Workday, and mentor junior analysts.

The AV Safety Analytics Engineer will develop data analytics infrastructure for automated vehicle safety, utilizing cloud processing and statistical methods. Responsibilities include creating data visualizations, monitoring metrics, and ensuring data integrity across systems.

The Revenue Strategy & Planning Manager will develop actionable growth plans by analyzing data, building reports, and collaborating with Sales leaders to enhance B2B revenue strategies.

As a Staff Data Engineer, lead the technical roadmap for data infrastructure, manage complex data architectures, and mentor engineers while driving the organization's data platform innovations.

Lead end-to-end data science initiatives: design, deploy, and monitor production ML models for credit risk, pricing, collections, and fraud. Partner cross-functionally to integrate models, set development standards, mentor team members, and maintain reliable, scalable machine learning pipelines and monitoring systems.