Health Research Data Specialist

Michigan Medicine; Ann Arbor (MI); Hybrid; USD 90,000 - 120,000; Full time

Job summary

University of Michigan's AI & Digital Health Innovation initiative seeks a Research Health Data Specialist to translate scientific questions into data strategies and build a robust data documentation system for reproducible research. You will enable investigators to access high-value datasets, design scalable data workflows, and support publications and grants.

You will collaborate with the Data & AI Solutions team to develop innovative data solutions that advance digital health research while

Qualifications

• Doctoral degree in information science, data science, public health, computer science, biostatistics, epidemiology, or a related field.
• Exceptional candidates with a Master’s degree and relevant experience may be considered.
• Experience with data management, workflow design, and research data governance.
• Experience working with large, complex datasets (clinical, digital health, sensor, EHR, socioeconomic, genomic, etc.).
• Strong project management and organizational skills.
• Experience with IRB processes, human subjects research, and HIPAA compliance.
• Demonstrated ability to support research that leads to publications, grant submissions, or other high-impact deliverables.
• Excellent scientific writing and communication skills.
• Enthusiasm for digital health, AI, and interdisciplinary scientific research.

Responsibilities

• Apply scientific expertise to help investigators refine hypotheses, select appropriate data sources, interpret data structures, understand methodological constraints, and design studies that maximize scientific impact.
• Connect investigators with the appropriate health datasets, tools, technologies, and workflows to accelerate research and publication-ready results.
• Consult on study planning and research workflows to enable publishable, fundable, reproducible, and scalable research outcomes.
• Proactively identify barriers, bottlenecks, and opportunities around data to improve research workflows and data accessibility.
• Ensure data access and research activities align with IRBMED, HIPAA, and AI&DHI governance requirements, including support for IRBMED submissions to minimize delays.
• Stay current with digital health, biomedical informatics, and data science methods and literature to ensure consultations reflect state-of-the-art approaches.
• Collaborate with the Data & AI Solutions team to develop and promote innovative data solutions that improve the quality, reproducibility, efficiency, and impact of research across AI&DHI.
• Create study cohorts and computed concepts by defining inclusion/exclusion criteria, extracting relevant data elements, validating cohort integrity, and ensuring cohorts align with scientific aims and regulatory requirements.
• Develop, augment, and maintain comprehensive documentation for available cohorts, data, and resources.
• Organize health data resources into repositories (e.g., GitHub) to support transparency and reproducibility.
• Collaborate with technical teams to ensure data resources and tools align with scientific goals and support robust research outputs.

Skills

Data governance

Project management

Scientific writing

Communication skills

IRB processes

HIPAA compliance

Data integration

Clinical data experience

Education

Doctoral degree

Master's degree with relevant experience

Tools

Python

R

SQL

GitHub

Job description

Job Summary

The Research Health Data Specialist is a key member of the AI & Digital Health Innovation (AI&DHI) initiative at the University of Michigan, working within the Data & AI Solutions team and collaborating with partners across campus. This role is designed to maximize research output by helping investigators translate scientific questions into actionable data strategies, building a data documentation system that supports rigorous, reproducible, and scalable research, and enabling efficient access to high-value datasets. A central expectation of this role is the ability to integrate scientific expertise and methodological insight into consultations, enabling research projects to progress smoothly and produce high-quality publications, grant submissions, and digital health innovations. The interview process will include a research presentation that should demonstrate your ability to bridge scientific questions, health data resources, and operational execution to achieve a meaningful research outcome. While this role in our team won't directly participate in research studies, familiarity with conducting scientific research is required to fulfill the responsibilities of the role.

Responsibilities

Core Responsibilities

• Apply scientific expertise to help investigators refine hypotheses, select appropriate data sources, interpret data structures, understand methodological constraints, and design studies that maximize scientific impact.
• Connect investigators with the appropriate health datasets, tools, technologies, and workflows to accelerate research and publication-ready results.
• Consult on study planning and research workflows to enable publishable, fundable, reproducible, and scalable research outcomes.
• Proactively identify barriers, bottlenecks, and opportunities around data to improve research workflows and data accessibility.
• Ensure data access and research activities align with IRBMED, HIPAA, and AI&DHI governance requirements, including support for IRBMED submissions to minimize delays.
• Stay current with digital health, biomedical informatics, and data science methods and literature to ensure consultations reflect state-of-the-art approaches.
• Collaborate with the Data & AI Solutions team to develop and promote innovative data solutions that improve the quality, reproducibility, efficiency, and impact of research across AI&DHI.

Data Documentation

• Create study cohorts and computed concepts by defining inclusion/exclusion criteria, extracting relevant data elements, validating cohort integrity, and ensuring cohorts align with scientific aims and regulatory requirements.
• Develop, augment, and maintain comprehensive documentation for available cohorts, data, and resources.
• Organize health data resources into repositories (e.g., GitHub) to support transparency and reproducibility.
• Collaborate with technical teams to ensure data resources and tools align with scientific goals and support robust research outputs.

Required Qualifications

• Doctoral degree in information science, data science, public health, computer science, biostatistics, epidemiology, or a related field.
• Exceptional candidates with a Master's degree and relevant experience may be considered
• Experience with data management, workflow design, and research data governance.
• Experience working with large, complex datasets (clinical, digital health, sensor, EHR, socioeconomic, genomic, etc.).
• Strong project management and organizational skills.
• Experience with IRB processes, human subjects research, and HIPAA compliance.
• Demonstrated ability to support research that leads to publications, grant submissions, or other high-impact deliverables.
• Excellent scientific writing and communication skills.
• Enthusiasm for digital health, AI, and interdisciplinary scientific research.

Desired Qualifications

• Experience with workflow automation, high-throughput computing, or cloud/HPC environments.
• Experience with GitHub and documentation
• Proficiency with R, Python, or similar tools for data handling.
• Experience with AI and LLMs, including familiarity with model development, evaluation, and deployment in research settings.
• Experience training researchers or students.

Modes of Work

Positions that are eligible for hybrid or mobile/remote work mode are at the discretion of the hiring department. Work agreements are reviewed annually at a minimum and are subject to change at any time, and for any reason, throughout the course of employment. Learn more about the work modes.

Background Screening

Michigan Medicine conducts background screening and pre-employment drug testing on job candidates upon acceptance of a contingent job offer and may use a third-party administrator to conduct background screenings. Background screenings are performed in compliance with the Fair Credit Reporting Act. Pre-employment drug testing applies to all selected candidates, including new or additional faculty and staff appointments, as well as transfers from other U-M campuses.

Application Deadline

Job openings are posted for a minimum of seven calendar days. The review and selection process may begin as early as the eighth day after posting. This opening may be removed from posting boards and filled any time after the minimum posting period has ended.

U-M EEO Statement

The University of Michigan is an Equal Opportunity Employer. We are committed to providing an environment of mutual respect where equal employment opportunities are available to all applicants, including protected veterans and individuals with disabilities.