Health Research Data Specialist

A cover letter is required for consideration for this position and should be attached as the first page of your resume. The cover letter should address your specific interest in the position and outline skills and experience that directly relate to this position.

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.

• 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., Git Hub) to support transparency and reproducibility.
• Collaborate with technical teams to ensure data resources and tools align with scientific goals and support robust research outputs.

• 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.

Experience with workflow automation, high-throughput computing, or cloud/HPC environments.

• Experience with Git Hub 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.

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