NHS Human Services, Inc.

This job was posted by https://illinoisjoblink.illinois.gov : For more information, please see: https://illinoisjoblink.illinois.gov/jobs/12600239 Department

BSD PHS - Sanghavi Lab

About the Department

Public Health Sciences (PHS) is the home in the Biological Sciences Division to biostatistics, epidemiology and health services research. These core fields in public health research share a focus on the development and implementation of complex analytic methods to understand the determinants of health, the efficacy of experimental treatments, and the structure of health care at the population level. Bringing together these fields in one department underscores their commonality and enhances opportunities for interdisciplinary research. Faculty members lead local, national, and international studies, and also welcome opportunities to collaborate with faculty across the Biological Sciences Division and the university. Substantively, our research themes include social and environmental determinants of health, genetics and disease, the economics of health care, and the evaluation and implementation of new technologies in public health and clinical care. In terms of methodological expertise, areas in which our faculty has developed innovative approaches include: risk factor measurement; multilevel, clustered and longitudinal data; clinical trials; administrative health data; social networks; and statistical methods to assess the genetic and molecular basis of disease.

Job Summary

The University of Chicago seeks an experienced research data scientist to join our health policy research team. The ideal candidate will have a Master\'s degree in health policy, public policy, economics, or a related field, with extensive experience analyzing Medicare and Medicaid claims data. The position requires expertise in both descriptive and causal inference statistical methods, strong programming skills in Python, R, and Stata (with working knowledge of SAS), and experience with high-performance computing and GitLab version control. The candidate must demonstrate a commitment to reproducible research practices, including thorough documentation, code testing, and validation. Experience with protected health information (PHI), HIPAA compliance, and data security protocols is essential. The role requires strong domain knowledge of U.S. healthcare systems, CMS policies, and medical coding systems. The successful candidate will have demonstrated scholarly writing ability, experience with grant-funded research, and the ability to manage multiple concurrent projects while mentoring junior team members. We seek someone with exceptional attention to detail, strong analytical and communication skills, and the ability to work both independently and collaboratively within interdisciplinary teams. Experience with IRB processes, data use agreements, and academic publication processes is highly desirable.

This at-will position is wholly or partially funded by contractual grant funding which is renewed under provisions set by the grantor of the contract. Employment will be contingent upon the continued receipt of these grant funds and satisfactory job performance.

Responsibilities

Lead complex analyses of Medicare and Medicaid claims data, including developing analytical plans, implementing statistical models, and ensuring reproducibility of results.

Design and execute both descriptive analyses and causal inference studies using large healthcare datasets, with a focus on health policy questions.

Manage data security and compliance requirements, including implementing HIPAA protocols and maintaining data use agreements.

Create and maintain well-documented, efficient code for data processing and analysis using Python, R, and Stata.

Coordinate with team members on GitLab for version control and collaborative coding projects.

Draft manuscripts for peer-reviewed publ cations, including methods sections, results, and data visualization.

Conduct thorough literature reviews to inform research design and contextualize findings.

Mentor junior team members in data analysis techniques, coding practices, and research methods.

Contribute to grant applications by providing technical expertise and preliminary analyses.

Develop and maintain data processing pipelines for ongoing research projects.

Implement quality control procedures, including code testing and validation of analytical results.

Participate in regular team meetings to present findings and coordinate research activities.

Has a deep understanding of methods to analyze complex data sets for the purpose of extracting and purposefully using applicable information. May develop and maintain infrastructure that connects data sets.

Calibrates data between large and complex research and administrative datasets. Guides and may set the operational protocols for collecting and analyzing information from the University\'s various internal data systems as well as from external sources.

Designs and evaluates statistical models and reproducible data processing pipelines using expertise of best practices in machine learning and statistical inference. Provides ex