Department

BSD PED – Infectious Diseases – Vaccine Center of Excellence/ID Health Disparities

About the Department

The section of Pediatric Infectious Disease provides comprehensive inpatient and outpatient care for children suffering from a wide variety of infectious diseases. Infectious disease specialists are experts in diseases caused by bacteria, viruses, fungi, and parasites.

This position will provide high-level epidemiology and data analysis support for Lilly Cheng-Immergluck’s Lab. Dr. Cheng-Immergluck’s lab undertakes data science and epidemiological research to advance knowledge about prevention of specific infectious diseases, which have a propensity for developing antibiotic resistance, e.g., Staphylococcus aureus and Streptococcus pneumoniae, as well as those pathogens which have contributed to epidemic outbreaks, are more likely to severely infect children, and are often times, vaccine preventable, e.g., RSV, measles, pertussis, etc. The mission of the work of this multi-disciplinary team is to explore the intersection of public health epidemiology and primary prevention care models with the goal to improve health outcomes of populations at greatest risk for vaccine preventable condition. Her lab uses spatial statistical models, geographic information system tools, bioinformatics, and artificial intelligence to predict communities most at risk for existing and emerging infectious diseases. This position will also play a key role in the Vaccine Center of Excellence which advances public health via research, advocacy and education of providers and the community served by UChicago; community based participatory research and engagement are cornerstones of this Center’s work.

Job Summary

The Senior Epidemiology Data Scientist will work in collaboration with Dr. Lilly Cheng-Immergluck’s to design and implement research studies focused on infectious disease prevention. In this capacity, the incumbent plays a key role in the design of research studies and their analysis plans. Responsibilities include overseeing study design, providing input on project protocols, and mentoring more junior research staff. The ideal candidate not only brings extensive experience in statistical analysis and study design for clinical research using data from electronic medical records but also should be able to perform the programming needs required by the project. This individual will also have had experience with scientific writing, which is required for funding applications and manuscripts for scientific and biomedical peer-reviewed journals. The incumbent will be a key contributor across a number of projects in the Immergluck Lab portfolio, with a particular focus on the intersection of clinical and population health data. They will work collaboratively with specialists in clinical data standards, data operations, and technology to bring together clinical and non-clinical data to support research studies to inform prevention of infectious diseases.

Responsibilities

• Leads development of data products according to specifications determined through collaborative meetings with PIs and co-investigators.
• Oversees and organizes data quality control programs and maintains clear provenance of clinical and non-clinical data during the joining and analysis of datasets.
• Applies their deep understanding of biostatistical methods to analyze complex and large data sets for the purpose of extracting and using applicable information.
• Develops and maintains infrastructure that supports integration of data across sources, e.g., clinical data, biospecimens, and administrative data.
• Designs and evaluates statistical models and reproducible data processing pipelines using expertise and best practices in statistical inference. Provides expertise for high-level or complex data-related requests and engages other internal resources as needed.
• Partners with collaborating teams at external institutions to support the overall data science needs of projects.
• Supports interpretation and visualization of results for manuscripts, presentations, and grant applications.
• Contributes substantively to grant applications, including methods, analytic plans, and preliminary data.
• Contributes substantively to development of manuscripts and presentations at national meetings.
• Participates in the scientific and scholarly education of learners, including students at the undergraduate, graduate, and post-doctorate levels.
• Leads and develops methods to analyze complex data sets for the purpose of extracting and purposefully using applicable information. Develops and maintains infrastructure that connects medium to large complex data sets.
• Provides expertise to staff or faculty members in defining the project and applies principals of data science in manipulation, statistical applications, programming, analysis and modeling.
• Recommends process improvements for data calibration between large and complex research and administrative datasets. Implements and may improve upon the established operational protocols for collecting and analyzing information from the University's various internal data systems as well as from external sources.
• Leads the design and evaluation of statistical models and reproducible data processing pipelines using expertise of best practices in machine learning and statistical inference. Provides expertise and/or recommends process improvements for high level or complex data-related requests and engages other IT resources as needed. Establishes partnerships 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 7+ years of work experience in a related job discipline.

Certifications:

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Preferred Qualifications

Education:

Master’s degree (MPH, MS) in public health, epidemiology, or health services research.

Experience:

At least 7-10 years of experience in analyzing large clinical datasets from both public and private health sectors. At least 7 years of experience working with data from the Epic electronic medical record system. At least 5-7 years of experience in supporting development of research methods for key healthcare system programs, including those programs designed to improve quality improvement and infectious disease prevention. At least 5 years of experience in supporting scientific manuscript development. At least 5-7 years of experience in designing and planning coordination with physician scientists in the healthcare setting. Demonstrated experience in developing data products for analysis and in liaising between researchers and technical staff. Prior experience with biomedical data analysis (e.g. biostatistics). Demonstrated leadership to supervise other statistical team members in the healthcare setting including other statisticians, system reporting specialist, and population health epidemiologists. Prior leadership in public health epidemiology. Previous experience with community-based organizations and working with community partners to improve health and wellness in community settings.

Preferred Competencies

• Proven ability to lead multidisciplinary teams and manage cross-sector collaborations.
• Ability to apply advanced epidemiological principles to infectious disease surveillance, outbreak investigation, and risk factor analysis.
• Ability to design and execute spatial statistical models to identify geographic patterns of disease risk at the community level.
• Ability to build and validate predictive models to identify communities most at risk for existing and emerging infectious diseases.
• Ability to contribute to peer-reviewed publications, grant applications, and technical reports; translate complex findings for clinical, community, and policy audiences.
• Ability to manage multiple research projects and timelines concurrently while maintaining scientific rigor and meeting deliverable commitments.
• Demonstrated experience using quantitative analysis tools (SPSS, STATA, R, SQL, and SAS). Advanced knowledge of epidemiology and statistical analysis methodology.

Working Conditions

Hybrid environment allowing remote work and work with remote stakeholders.

Application Documents

• Resume (required)
• Cover letter (Required)
• Writing sample required

The University of Chicago uses AI-assisted tools to streamline and augment some recruitment processes; however, AI is not used to make hiring decisions.

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

FLSA Status

Exempt

Pay Range

$110,000.00 - $150,000.00

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.

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