Well-Being Data & Evaluations Analyst

Michigan Medicine; Ann Arbor (MI); On-site; USD 110,000 - 150,000; Full time

Job summary

Michigan Medicine seeks a Senior Well-Being Data & Evaluations Analyst to build, maintain, and evaluate AI-assisted data ecosystems supporting a mixed portfolio of applied and academic research. The role emphasizes data structuring, integration, and governance for high-volume longitudinal well-being data including EHR sources.

The position works with minimal supervision, partnering with investigators and clinical teams to deliver actionable insights while ensuring data integrity, accessibility,

Qualifications

• Advanced regression, causal inference, longitudinal and time-series analyses.
• Reproducible statistical analyses with Python/R/SQL.
• Experience with EHR data and large unstructured datasets.

Responsibilities

• Design and maintain AI-assisted data infrastructures for well-being research.
• Develop analytic workflows with rigorous scientific methods.
• Create dashboards and reports for executive stakeholders.
• Mentor staff in data management and workflow development.
• Contribute to manuscripts and grant-related datasets.

Education

Master's degree or higher in a quantitative field

Tools

Python

R

SQL

Tableau

Power BI

Job description

The Senior Well-Being Data & Evaluations Analyst will build, maintain, and evaluate AI-assisted database ecosystems to support a high-volume, methodologically rigorous applied/non-academic (~80%) and academic (~20%) research portfolio within the Office of Well-Being. This role focuses on structuring, integrating, and managing complex institutional sources of high-volume longitudinal well-being data, including -- but not limited to -- electronic health records (EHR). This role serves as the data manager for Office of Well-Being-managed data assets, working in close partnership with the Office of Well-Being data steward to operationalize stewardship decisions, implement technical specifications, and produce the technical documentation that supports Michigan Medicine's data governance program. The work aligns with and supports the Office's goal to empower all individuals and teams who investigate, work, and learn at Michigan Medicine to use and build their expertise to drive our purpose of seamlessly advancing healthcare as a team. Specifically, this role will help facilitate the achievement of one of the Office of Well-Being's key goals, which is to evaluate healthcare workforce technologies, AI-enabled systems, organizational interventions, and other human-centered technologies for their ability to improve the well-being of the healthcare workforce.

The individual will work with minimal supervision and exercise independent judgment in workforce data infrastructure, management, analytics, and reporting. This position partners closely with investigators, analysts, and clinical teams to produce actionable insights and ensure data integrity, accessibility, and alignment with research and operational objectives and stakeholder needs. Assignments are broad in scope and require originality, ingenuity, and independent decision-making.

Responsibilities:

Data Infrastructure, Design & Management (40%)

• Evaluate and design database structures to support research and operational data needs for the Office of Well-Being, including informing strategic leadership decisions.
• Design and maintain data infrastructure using EHR and institutional data sources, particularly those aimed at evaluating AI functionalities.
• Translate operational workflows into structured data elements.
• Design and maintain processes for data extraction, transformation, and storage.
• Build and maintain data pipelines for large, complex datasets.
• Perform data cleaning, validation, and reconciliation across multiple sources.
• Ensure data quality, integrity, and compliance with institutional and regulatory standards.
• Conduct ongoing validation of data infrastructure to ensure accuracy and usability, inclusive of data dictionaries and data flow diagrams.
• Create and maintain technical documentation for Office of Well-Being-managed data assets in collaboration with the Office of Well-Being data steward, including data dictionaries, logical and physical data models, pipeline documentation, and lineage maps. Document the authoritative sources for the Office of Well-Being managed datasets.
• Support the Office of Well-Being data steward in maintaining Office of Well-Being-managed data assets in the institutional data asset catalog.
• Work with the Office of Well-Being data steward to identify and define critical data elements, terms, and metrics for Office of Well-Being-managed data assets, and ensure data steward-approved definitions are submitted to and maintained in the enterprise data glossary.
• Collaborate with data infrastructure teams (DBAs) on system availability, maintenance, and disaster recovery for Office of Well-Being-managed data assets.

Analytics & Statistics (25%)

• Develop and direct AI-assisted analytic workflows with validated scientific rigor for current-state evaluation of well-being across institutional populations, including faculty, staff, and learner communities, using multiple data sources with an emphasis on statistical analysis of EHR and operational healthcaredata.
• With the support of AI tools, deploy statistical and analytical methods for evaluating healthcare technologies and AI-enabled interventions, including:
• 1) multivariable regression modeling
• 2) longitudinal and mixed-effects modeling
• 3) causal inference methods for observational healthcare data
• 5) machine learning evaluation, validation, and calibration
• Prepare analysis-ready datasets in collaboration with other technical staff, faculty investigators, and stakeholders, including senior MM leadership.
• Support transparent and reproducible analytic pipelines and workflows.

Visualization and Reporting (20%)

• Design and maintain dynamic AI-enabled operational intelligence systems (e.g., interactive dashboards) that translate well-being, culture, and workforce metrics into actionable insights for operational and executive stakeholders.
• Partner with subject matter experts to translate clinical/workforce context into effective narrative visualizations and data stories.
• Create and maintain reporting documentation (metric definitions, data lineage, refresh schedules, assumptions, limitations) to support transparency and reproducibility, ensuring dashboards and reports reflect data steward-approved data definitions and documented lineage.
• Build executive-ready summaries (one-page briefs / slide-ready visuals) that synthesize findings, implications, and opportunities.
• Support the development of datasets for grants and manuscripts.
• Create publication-ready tables and figures.
• Maintain documentation of internal and external use of Office of Well-Being data and reporting outputs to support the data steward's oversight of appropriate use and access.
• Collaborate with clinicians, researchers, and operational leaders across departments; in doing so, represent the research arm of the Office of Well-Being in institutional conversations about evaluating healthcare workforce technologies.
• Participate in Data Governance Program workgroups and subcommittees, contributing technical expertise to improve institutional data standards, infrastructure, and practices.
• Implement data governance standards for EHR and institutional data sources, particularly those aimed at evaluating AI functionalities, including de-identification, consent alignment, and IRB/institutional compliance requirements.
• Provide access to the Office of Well-Being managed data in accordance with the data steward approval and the principle of least privilege, including deprovisioning on data user employment changes.
• Coordinate with Information Assurance on response to IT security incidents affecting protected or regulated Office of Well-Being data.
• Communicate and escalate data-related questions, issues, or conflicts to the appropriate data steward.
• Partner with the Office of Well-Being program and project leads to connect findings to active well-being initiatives, enabling data-informed program design and iteration.
• Serve as a technical resource for database design and data management best practices.
• Mentor research staff in data management and workflow development.
• Contribute to manuscripts, with an emphasis on those focused on the evaluation of AI-enabled healthcare systems and organizational transformation.
• Prepare statistical methods/results sections and respond to reviewer comments.
• Clearly communicate findings to clinicians, trainees, and multidisciplinary collaborators.
• Participate in the Trusted Data Manager community to maintain alignment with Michigan Medicine Data Governance, supporting enterprise-wide data management standards, documentation practices, and stewardship coordination.

Required Qualifications:

• Master's Degree or higher in Computer Science, Data Science, Health or Clinical informatics, Computational Social Science, Industrial-Organizational Psychology, Organizational Behavior, Human-Computer Interaction, Systems Engineering – OR - a related quantitative social scientific field with demonstrated experience in data-intensive and sociotechnical work
• 5-7 years post-graduate experience
• Proficient in advanced regression modeling, causal inference, longitudinal, and time series analyses, machine learning evaluation, quasi-experimental design, missing data, and EHR data quality, survival analysis
• Proficient in conducting reproducible statistical analyses using Python, R, and/or SQL (proficiency in multiple preferred)
• Proficient in dashboarding and visualization tools (e.g., Tableau, Power BI)
• Experience working with large, complex, unstructured datasets (e.g., EHR data)
• Excellent organizational, analytical, and communication skills, particularly when communicating results to a diverse set of stakeholders.

Desired Qualifications:

• Experience evaluating healthcare workforce technologies, AI-enabled systems, organizational interventions, workflow redesign, or other human-centered technologies in the context of the healthcare workforce
• Familiarity with well-being measurement frameworks (e.g., PROMIS, Stanford WellMD model) or measurement of occupational health constructs (e.g., burnout, job satisfaction, cognitive load, work stressors, turnover intentions)
• Familiarity with implementation and organization evaluation methods and design principles
• Familiarity with data governance principles and practices (e.g., data stewardship, data lineage, metadata management, data cataloging)
• Experience producing technical documentation (data dictionaries, data models, lineage documentation) to support institutional data governance programs