Top Remote Data & Analytics Jobs in Detroit, MI

The Lead Risk Analytics Data Scientist will develop risk pricing, design data models, automate workflows, and communicate insights to leadership. Responsibilities include owning the risk narrative, building analytics foundations, and utilizing AI to enhance efficiency.

Perform data extraction from SQL/NoSQL systems, validate and transform data, and run statistical and psychometric analyses (IRT, reliability, norming). Validate assessment logic, define quality metrics, design research and simulation studies, and present findings via reports and dashboards. Collaborate cross-functionally and maintain current psychometric and data engineering practices.

Design, develop, and maintain Power BI dashboards and automated reports for financial analytics. Ensure data accuracy, build data models and ETL pipelines, write/optimize SQL and DAX, analyze trends to provide insights, and collaborate with stakeholders to define reporting requirements and best practices.

Contract Tableau expert to design, build, and improve dashboards from source data. Model complex datasets, optimize performance and usability, establish dashboard best practices, and partner with stakeholders to deliver actionable reporting (30–35 hours/week, contractor).

Transcribe Assamese video content with timestamps, analyze speaker tone and intent, evaluate grammar and idioms, perform semantic and contextual analysis, collaborate to improve processes, and maintain confidentiality and accuracy.

Design and maintain enterprise revenue-cycle KPIs and dashboards, analyze multi-source RCM datasets, build predictive models, identify process improvements, support audit/compliance, lead stakeholder working groups, author executive deliverables, follow PM best practices, mentor junior analysts, and safeguard PHI/PII under DoD controls.

Lead analytical projects, develop quantitative models, analyze datasets for insights, and translate findings into business recommendations, primarily supporting client decision-making.

Design, build, and own end-to-end data systems and pipelines powering payments, fraud response, and reporting. Implement streaming and batch integrations, APIs, and workflow orchestration. Define data correctness with monitoring, alerting, and tests. Create reusable patterns and collaborate with stakeholders to clarify requirements and tradeoffs.

Design and deliver scalable, reliable data services and microservices for real-time and batch processing. Build REST APIs, model data across SQL/NoSQL stores, drive data quality, own deployment/monitoring/incident response, set technical standards, and mentor engineers.

Lead development and maintenance of go-to-market analytics and category management tools to optimize pricing, promotion, distribution, displays, and shelf placement. Provide actionable insights using POS and syndicated data, present business reviews to clients, track KPIs across retailers, and support sales and marketing objectives across omnichannel channels.