Top Remote Data & Analytics Jobs in Detroit, MI

Maintain and improve eligibility and claims data feeds, analyze feed performance and errors, troubleshoot discrepancies, work with external feed providers to map and test integrations, and help improve internal tools, documentation, and processes.

Lead analytics implementations using Google Analytics and Tag Manager for clients, providing mentorship and managing projects with a focus on enhancing clients' digital strategies.

The Senior Data Engineer II will design and maintain scalable data pipelines, collaborate with teams on machine learning workflows, and ensure data accessibility across systems.

The Analytics Engineer will manage BI modeling, create dashboards, ensure data quality, and collaborate with customers and teams for reporting needs.

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

The Financial Systems Analyst manages NetSuite, optimizes business system performance, supports users, ensures data integrity, and collaborates on process improvements.

Lead Pager Health’s enterprise data and reporting strategy, build a centralized data and reporting shared services function, own internal dashboards and client-facing analytics, partner with Product and Engineering on data architecture and governance, mentor data teams, prioritize and deliver data initiatives, and drive continuous improvement in tooling, automation, and data storytelling to support growth, efficiency, and client outcomes.

Design, develop, and maintain forecasting and optimization models for contact forecasting, headcount planning, staffing and budget. Partner cross-functionally to produce data-driven recommendations, present to leadership, and mentor forecasting analysts.

As a Staff Analytics Engineer, you will bridge data engineering, data science, and business analytics to generate actionable insights, build data models, and empower stakeholders across the organization.

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.