Research Health Data Specialist Sr

Job Summary

The Data Solutions Manager & Research Health Data Specialst Senior is a key member of the AI & Digital Health Innovation (AI&DHI) initiative at the University of Michigan, working within the Data 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, enabling efficient access to high-value datasets, and building workflows that support rigorous, reproducible, and scalable research.

A central expectation of this role is the ability to integrate scientific expertise and methodological insight into consultations, ensuring that research projects progress smoothly and produce high-quality publications, grant submissions, and digital health innovations. The interview process will include a research presentation that must 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, deep familiarity with conducting scientific research is required to fulfil the responsibilities of the role.

• Guide researchers through compliant acquisition of clinical, biospecimen, genomic, and other health datasets.
• Create study cohorts and compute concepts by defining inclusion/exclusion criteria, extracting relevant data elements, validating cohort integrity, and ensuring cohorts align with scientific aims and regulatory requirements.
• Maintain comprehensive documentation for available cohorts, data, and resources.
• Ensure researchers  requests for data access adhere to IRBMED, HIPAA, and AI&DHI governance requirements.
• Integrate scientific expertise into consultations to help investigators refine hypotheses, select appropriate data sources, and design studies that maximize scientific impact.
• Consult on study planning with a focus on enabling publishable, fundable, and scalable research outcomes.
• Provide support for IRBMED submissions to prevent delays in research timelines.

Monitor project progress and proactively identify barriers that could impact research deliverables.

• Apply scientific reasoning to help investigators interpret data structures, understand methodological constraints, and choose approaches that strengthen research findings.
• Support investigators by connecting them with the right tools, datasets, and workflows to accelerate publication-ready results.
• Stay current with digital health and data science literature to ensure research consultations reflect state-of-the-art methods.
• Develop innovative data solutions that enhance the quality, reproducibility, and impact of research across AI&DHI.

Promote research productivity by identifying opportunities to streamline processes, reduce bottlenecks, and improve data accessibility.

• Develop, refine, and document standardized data workflows, reproducible pipelines, and efficient data processing strategies.
• Organize health data resources into repositories 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 oral 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.

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.

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