Top Data & Analytics Jobs in Detroit, MI

The Monetization Analyst will enhance team performance, drive efficiency across monetization, support, and online reputation, and implement AI-driven automation solutions.

The GIS Coordinator designs and maintains spatial systems for municipal clients, ensuring data integrity and integrating GIS with other government systems. Responsibilities include geospatial analytics, training users, and producing maps for decision-making.

The Senior Data Engineer will build and maintain ETL/ELT pipelines, optimize SQL queries, and collaborate on machine learning workflows, driving data engineering best practices.

Manage a team of data scientists to develop fraud detection models, lead planning and collaboration with leadership, and communicate outcomes effectively while maintaining strong business intuition and technical leadership.

The Lead Data Platform Engineer will design and build Virtuous's data platform, ensuring it is secure, scalable, and AI-ready. Responsibilities include architecting access controls, optimizing performance, and enabling self-service analytics while maintaining governance and compliance standards.

As a Senior Business Systems Analyst, you'll manage SaaS portfolios, identify AI automation opportunities, gather requirements for engineering, and provide insights into cross-functional initiatives.

The Capital Analytics Lead will analyze capital, margin, liquidity, and counterparty risk, support regulatory engagement, and produce insights for senior leadership.

As a Senior Yield Manager, you'll optimize digital advertising revenue through effective pricing and inventory strategies, utilizing AI insights for improved forecasting and performance.

The role involves leading the enterprise strategy for data analytics and technology modernization, driving data-driven decision making, and managing digital transformation initiatives while building a high-performing team.

The Staff Data Engineer will lead financial data operations, ensuring accuracy and reliability of data pipelines, while collaborating with Finance, Product, and Engineering teams to enhance systems and decision-making processes.