Top Analytics Jobs in Ann Arbor, MI

As a Data Scientist in Customer Analytics, you will analyze customer data, measure Cresta's impact through experiments, and build dashboards for insights. You'll collaborate with cross-functional teams and support strategic customer initiatives.

As an Analytics Engineer, transform raw data into metrics, manage data pipelines, ensure data integrity, and collaborate with teams for analytics solutions.

Lead a centralized Decisioning Analytics team to measure and optimize Pega-powered Next-Best-Action decisioning. Own measurement frameworks, ADM performance, transparency, governance, cross-functional partnerships, NBA analysis, roadmap, and team development to drive measurable customer and financial outcomes.

The Data Architect will design scalable data models, transform raw data into actionable insights, and deploy analytics solutions for healthcare providers, with a strong emphasis on SQL proficiency and cloud technologies.

The Analytics Lead will guide data-driven decision-making, develop measurement frameworks, forecast revenue, and mentor an analytics team, ensuring effective communication of insights.

Lead Oracle Cloud reporting initiatives, ensuring data accuracy and performance while mentoring junior analysts and collaborating with stakeholders.

The Senior Data Analytics Engineer will develop data pipelines, support data-driven decision-making, collaborate with stakeholders, and improve data quality and processes.

Lead analytical efforts in Real World Data research, develop specifications and methodologies, manage projects, and communicate findings to stakeholders.

The Senior Data Scientist will build frameworks for data-driven decision making, develop predictive models, ensure data quality, and enhance customer experiences through analytics and operational insights.

As an Analytics Lead, you will manage the measurement strategy, oversee tracking architecture, design testing frameworks, and create insights for clients. You'll lead a junior specialist and ensure data integrity across client accounts. Responsibilities include building dashboards, interpreting metrics, and creating measurement plans to drive business decisions.

Lead architecture and execution of Grafana's marketing data stack and analytics practice. Build forecasting, predictive, and causal models, design BigQuery schemas, deploy agentic AI and automated pipelines, partner with GTM leadership, and deliver executive-grade insights to drive demand generation and ROI.

Design, build and maintain scalable, auditable analytics-ready data models (primarily in dbt) for PBM and care navigation. Implement data quality checks, CI/CD, documentation, pipeline orchestration, and partner with stakeholders to enable self-service analytics and AI-enabled workflows.

The Senior Analytics Engineer will design and build scalable data pipelines using dbt and Snowflake, providing technical guidance, conducting audits, and mentoring junior engineers while leveraging AI tools to enhance productivity.

The Data Scientist will enhance statistical models for forecasting, analyze datasets for operational planning, and communicate insights to stakeholders.

Lead analytics and data science initiatives to drive measurable business outcomes across various functions, including Underwriting and Claims. Oversee the full analytics lifecycle, foster stakeholder engagement, and mentor junior team members.

Lead analytics for Omnichannel retail/restaurant/CPG partners: act as primary analytics contact for Sales, manage analytic engagements and team capacity, perform SQL-based analysis, craft data-driven narratives and recommendations, influence campaign strategy, represent analytics in cross-functional settings, and automate insights using AI-enabled tools.

Lead design and delivery of Workday reporting and analytics for AMS clients: build advanced/matrix/trending/composite reports, dashboards, Discovery Boards, Worksheets, and complex calculated fields; gather requirements, optimize reports, mentor junior consultants, and provide Workday best-practice guidance.

Trace3 seeks enthusiastic individuals for its Data Intelligence Team. The role focuses on innovative technology solutions and consulting services, fostering a collaborative and dynamic work environment.

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.

Lead a high-impact analytics function partnering with clinical and business leaders to apply advanced analytics, modeling, and BI to improve participant outcomes, operations, and financial performance. Manage and mentor analysts, design analyses and models, build stakeholder relationships, and communicate actionable insights to influence enterprise strategy.

Design, build, and maintain production ETL/ELT pipelines, analytics-ready data models, and cloud data infrastructure. Improve pipeline performance, data quality, governance, and migrate legacy systems to modern platforms. Collaborate with stakeholders to translate business requirements into reliable data products for project, financial, and operational analytics.

Design and implement end-to-end data solutions on Microsoft Fabric and Azure for insurance datasets. Build ingestion pipelines (batch, streaming, API), Lakehouse architectures, and data models. Enforce governance, security, compliance, and cost/performance optimizations while guiding engineers and stakeholders on architectural best practices.

As a Senior Analyst, you will develop data-driven solutions in collaboration with operations teams and deliver insights through reports and dashboards to improve patient outcomes.

Design and implement end-to-end Microsoft Fabric data solutions (ingestion, Lakehouse, warehouse, real-time). Create architectures, reusable frameworks, and governance patterns for insurance datasets. Lead ingestion pipelines, optimize performance/cost, integrate with Azure services and security, support regulatory compliance, and provide technical direction to engineering and analytics teams.

Design and build scalable data foundations, pipelines, semantic models, and AI-grounded tooling. Lead platform-level AE tooling, reusable dbt patterns, observability, cost strategies, and agentic AI workflows. Partner with stakeholders, guide team technical development, and drive cross-functional data initiatives from ambiguous problems to production.