Top Data & Analytics Jobs in Ann Arbor, MI

Design and implement OBIEE solutions and enterprise dashboards for capital markets. Develop RPD models and presentation layers, apply dimensional modeling and data warehouse best practices, enforce OBIEE security/roles, produce technical specifications, prioritize tasks, and liaise with clients and IT teams to gather requirements and deliver analytics solutions.

Analyze business domains and document processes, requirements, and data models. Gather business and technical environments, develop BRDs and technical requirements, support UAT and SDLC activities, coordinate testing and production updates, manage multiple projects, and present findings to stakeholders.

Design and deploy object detection and tracking modules; develop ML algorithms; guide team members; improve data governance and quality assessment.

Design and own end-to-end data domains and pipelines for plant sensors, lab results, and historians. Build schemas, ensure data quality, observability, and lineage, and supply model-ready time-series and analytical datasets to ML and operations teams. Mentor junior engineers and define data contracts.

Analyze banking and financial crime data, develop and tune statistical and machine learning classification models, evaluate model performance, identify data quality issues, and recommend optimization strategies. The role uses Python and SQL for feature engineering, statistical modeling, automation, and large-scale data processing. Responsibilities also include documenting methodologies, reporting results to management and regulators, and collaborating with compliance, technology, business, and model governance teams.

Leads analytics, reporting, data modeling, automation, AI-agent development, and quality assurance for revenue cycle operations. Creates Power BI and Excel reports, analyzes complex healthcare and operational datasets, develops scalable warehouse models, and delivers actionable insights to leadership. Uses SQL, cloud data platforms, Git, Python, and AI-assisted tools to improve reporting efficiency, accuracy, and decision support.

Senior Decision Scientist partners with product, engineering, marketing, and finance teams to guide roadmaps, resource allocation, and business strategy. The role uses experimentation, causal inference, statistical analysis, metric strategy, and user behavior research to produce actionable recommendations. Responsibilities include establishing causality, improving instrumentation, communicating insights to leadership, and using AI tooling to scale analytical workflows. The position requires consumer technology analytics experience, strong SQL and Python or R skills, and expertise in experimentation and causal inference.

Designs and leads enterprise data pipelines and integrations across Snowflake, Oracle Fusion, on-premises databases, and cloud applications. Ensures data quality, governance, security, scalability, and performance while supporting Power BI, AI/ML, and self-service analytics. Partners with business and technical stakeholders, leads migrations, mentors engineers, optimizes data platforms, and establishes engineering best practices.

Designs, builds, deploys, and maintains scalable data pipelines, data lake systems, batch and streaming processing, MLOps frameworks, data documentation tooling, and an agentic data exploration platform. The role supports customer-facing products and enables engineering teams through reliable infrastructure, observability, data discovery, and AI capabilities.

Build and operate scalable batch and streaming data pipelines, data lake infrastructure, MLOps frameworks, data documentation systems, and an AI-assisted data exploration tool. The role focuses on reliable data ingestion, large-scale processing, workflow orchestration, observability, and infrastructure supporting customer-facing products and machine learning development.

Leads CookUnity’s Advanced Analytics function, setting strategy and partnering with Product, Finance, Operations, Marketing, and leadership. The role involves experimentation, causal inference, retention and growth analysis, marketing measurement, product impact evaluation, and translating insights into business decisions. This hands-on player-coach will guide complex analytical work, develop analysts, improve analytical rigor, and shape team structure and priorities.