Health Policy Data Science Specialist

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 publications, 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 expertise for high level or complex data-related requests and engages other IT resources as needed. Partners with other campus teams to assist faculty with data science related needs.

• Performs other related work as needed.

Minimum Qualifications

Education:

Minimum requirements include a college or university degree in related field.

Work Experience:

Minimum requirements include knowledge and skills developed through 5-7 years of work experience in a related job discipline.

Certifications:

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Preferred Qualifications

Education:

• Master's degree in health policy, public policy, economics, or a related field.

Experience:

• 5+ years working with Medicare and Medicaid claims data and advanced statistical computing to answer health policy research questions.

• 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.

• Demonstrated 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.

• Strong domain knowledge of U.S. healthcare systems, CMS policies, and medical coding systems.

• Demonstrated scholarly writing ability, experience with grant-funded research, and the ability to manage multiple concurrent projects while mentoring junior team members.

• Experience with IRB processes, data use agreements, and academic publication processes is highly desirable.

Preferred Competencies

• Exceptional attention to detail, strong analytical skills, and the ability to work both independently and collaboratively within interdisciplinary teams.

• Demonstrates advanced proficiency in analyzing large administrative healthcare datasets, particularly Medicare and Medicaid claims data.

• Shows expertise in implementing statistical methods for both descriptive analyses and causal inference studies.

• Exhibits strong programming capabilities in multiple languages (Python, R, Stata, SAS) with an emphasis on efficient and reproducible code.

• Displays thorough understanding of healthcare delivery systems, payment models, and CMS policies.

• Shows mastery in handling protected health information (PHI) and maintaining HIPAA compliance throughout research processes.

• Demonstrates ability to develop and maintain clear documentation of analytical processes and research methodologies.

• Exhibits strong project management skills, including the ability to coordinate multiple research projects simultaneously.

• Shows proficiency in scholarly writing and manuscript preparation for academic publications.

• Demonstrates mentorship capabilities and ability to train others in technical and analytical skills.

• Exhibits meticulous attention to detail in data analysis, code testing, and validation procedures.

• Shows strong problem-solving abilities when addressing complex analytical challenges.

• Demonstrates effective communication skills with both technical and non-technical team members.

• Exhibits proficiency in version control and collaborative coding practices using GitLab.

• Takes initiative in handling meeting logistics, including scheduling, room reservations, and calendar management.

Working Conditions

• Primary work location is in an office environment on the University of Chicago campus.

• Regular use of computers and standard office equipment for extended periods.

• Work primarily involves analyzing data, writing code, and preparing research documents.

• Regular participation in team meetings and collaborative research discussions.

• Occasional deadlines requiring focused attention and extended work hours.

• Regular interaction with faculty, staff, and research team members.

• Access to and responsibility for sensitive health data requiring strict adherence to security protocols.

• Work schedule typically follows standard business hours with flexibility as needed for project deadlines.

• Must maintain required trainings for research compliance and data security.

• Regular use of technical infrastructure including high-performance computing resources.

Application Documents

• Resume (required)

• Cover letter (preferred)

When applying, the document(s) MUST be uploaded via the My Experience page, in the section titled Application Documents of the application.

Job Family

Research

Role Impact

Individual Contributor

Scheduled Weekly Hours

40

Drug Test Required

No

Health Screen Required

No

Motor Vehicle Record Inquiry Required

No

Pay Rate Type

Salary

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FLSA Status

Exempt

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Pay Range

$90,000.00 - $120,000.00

The included pay rate or range represents the University’s good faith estimate of the possible compensation offer for this role at the time of posting.

Benefits Eligible

Yes

The University of Chicago offers a wide range of benefits programs and resources for eligible employees, including health, retirement, and paid time off. Information about the benefit offerings can be found in the Benefits Guidebook.

Posting Statement

The University of Chicago is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender, gender identity, or expression, national or ethnic origin, shared ancestry, age, status as an individual with a disability, military or veteran status, genetic information, or other protected classes under the law. For additional information please see the University's Notice of Nondiscrimination.

 

Job seekers in need of a reasonable accommodation to complete the application process should call 773-702-5800 or submit a request via Applicant Inquiry Form.

 

All offers of employment are contingent upon a background check that includes a review of conviction history.  A conviction does not automatically preclude University employment.  Rather, the University considers conviction information on a case-by-case basis and assesses the nature of the offense, the circumstances surrounding it, the proximity in time of the conviction, and its relevance to the position.

 

The University of Chicago's Annual Security & Fire Safety Report (Report) provides information about University offices and programs that provide safety support, crime and fire statistics, emergency response and communications plans, and other policies and information. The Report can be accessed online at: http://securityreport.uchicago.edu. Paper copies of the Report are available, upon request, from the University of Chicago Police Department, 850 E. 61st Street, Chicago, IL 60637.