Job Summary
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 WellBeing 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 healthcare data
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,4) quasi-experimental evaluation designs, and 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.
Governance, Collaboration & Communication (15%)

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