Department
BSD OBG - Griffith Lab
About the Department
This position will be based in the Women’s Brain Health research program led by Dr. Francesca Farina, Assistant Professor in the Department of Obstetrics and Gynecology and a faculty member of the Healthy Aging & Alzheimer’s Research Care (HAARC) Center. Dr. Farina is a trained neuroscientist whose research focuses on modifiable factors that influence risk for Alzheimer’s disease and related dementias, with a particular emphasis on identifying risk and resilience factors that emerge during key life transitions, such as menopause. The HAARC Center, part of the Biological Science Division, seeks to serve as an aging and dementia research hub. The Biostatistical Core, led by Dr. Ana Capuano, is dedicated to applying state of science methods to the discovery of factors that promote resilience, resistance, and increased healthspan through multidisciplinary research. This work also involves collaboration with international research partners, offering opportunities to contribute to global initiatives in women’s brain health and Alzheimer’s disease prevention.
Job Summary
The job provides professional support and solves problems in collecting, organizing, and analyzing information from the University's various internal data systems as well as from external sources The job performs data analysis assignments related to data manipulation, statistical applications, programming, analysis and modeling in order to support projects.
The Data Scientist/Statistician I will implement the research analyses, executing large-scale data harmonization, statistical analysis, and modeling – including predictive models for Alzheimer’s disease with a particular focus on sex-specific (female) risk. The harmonization pipeline will integrate female-specific and cognitive variables/items from large population-based cohort studies and longitudinal secondary health datasets drawn from multiple U.S. and international sources. Responsibilities include acquiring, cleaning, and organizing datasets; mapping and assessing available cohorts and sources by profiling variable coverage, coding systems, and cognitive instruments; prototyping an end-to-end harmonization on subsets of several cohorts with documented variable/value maps and quality-control checks; validating measurement invariance and IRT linking on one to two cognitive scales to produce crosswalks and uncertainty summaries; and delivering an analysis-ready, versioned dataset accompanied by a data dictionary and a concise user guide. Success will be demonstrated by a documented harmonization pipeline with repeatable builds and validation, clear crosswalks and comparability statements for key cognitive measures, and a dataset stakeholders can use with confidence – complete with known limitations, provenance, and QC metrics. The role also includes preparing high-quality reports, visualizations, and peer-reviewed publications as needed. The Data Scientist will serve as a key analytical expert and integral team member, contributing specialized skills in data integration, statistical modeling, and interpretation to advance the project’s goals.
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
Leads the acquisition, cleaning, and harmonization of secondary datasets from the multiple sources, including international cohort studies, with support from Dr. Farina and the project team.
Conducts data exploration and statistical analyses to extract meaningful insights from large, complex datasets, with support from Dr. Farina, Dr. Capuano and the project team.
Unify different types of data including cognitive instruments (e.g., MMSE, MoCA, Trails, Digit Symbol, HVLT, etc.): perform measurement invariance testing; build IRT/linking models and score crosswalks; document comparability limits.
Correct site/batch effects and temporal drift using mixed-effects models, empirical Bayes approaches, and sensitivity analyses.
Handle missing with principled methods (e.g., MICE, IPW); quantify robustness.
Maintain privacy-conscious data handling (HIPAA/GDPR concepts).
Maintains and analyzes statistical models using best practices in machine learning, statistical inference, and reproducible research workflows.
Prepares publication-ready tables, figures, and statistical summaries for interim and final reports.
Develops tailored statistical procedures and visualizations for specific research questions.
Analyzes moderately complex data sets for the purpose of extracting and purposefully using applicable information.
Provides professional support to staff or faculty members in defining the project and applying principals of data science in manipulation, statistical applications, programming, analysis and modeling.
Cleans, transforms, merges, and matches between large and complex research and administrative datasets. Plans own resources to collect, organize, and analyze information from the University's various internal data systems as well as from external sources.
Builds and analyzes statistical models and reproducible data processing pipelines using knowledge of best practices in machine learning and statistical inference. Serves as a single point of contact for all requests and engages other IT resources to assist.
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 2-5 years of work experience in a related job discipline.
Certifications:
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Preferred Qualifications
Education:
Graduate college or university degree.
Masters degree in Biostatistics, Statistics, Epidemiology, Psychometrics, Data Science, or related field.
Experience:
Foundational knowledge and hands-on practice in core statistical methods – descriptive inference, probability, linear/logistic regression – with implementation in R/Python and clear interpretation.
Hands-on experience harmonizing cognitive assessment data and applying measurement invariance/IRT/score linking.
Practical knowledge of missing data (MICE, weighting).
Experience publishing harmonized datasets and reproducible reports (R Markdown/Quarto/Jupyter).
Foundational knowledge and hands-on practice in survival analysis, mixed-effects models and longitudinal modeling.
Experience with health data standards (ICD, SNOMED, LOINC, HL7 FHIR or OMOP) and unit/scale conversions (UCUM).
Preferred Competencies
Excellent written and oral communication.
Organization.
Problem-solving.
Collaboration.
Attention to detail.
Able to work autonomously.
Skills with a wide variety of digital collaboration tools (Zoom, MS Teams, etc.).
Proficiency in MS Office Suite.
Programming and Coding experience.
Working Conditions
Ability to use a computer for extended periods.
Office/Clinical Setting.
Application Documents
Resume (required)
Cover Letter (required)
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
Yes
Health Screen Required
Yes
Motor Vehicle Record Inquiry Required
No
Pay Rate Type
Salary
FLSA Status
Exempt
Pay Range
$70,000.00 - $100,000.00
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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Data & Analytics (1)