Data Scientist

Biological Sciences Division at the University of Chicago

This role 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 Data Scientist/Statistician I will implement 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.

Base pay range: $70,000.00 – $ per year.

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, serves as an aging and dementia research hub. The Biostatistical Core, led by Dr. Ana Capuano, applies state‑of‑the‑art methods to discover factors that promote resilience, resistance, and increased healthspan through multidisciplinary research.

The Data Scientist will acquire, clean, harmonize, and analyze secondary datasets from multiple international cohort studies and longitudinal health datasets. Responsibilities include building harmonization pipelines, validating measurement in variance, linking models, handling site and batch effects, missing data, and privacy‑conscious data handling. The role also involves preparing reports, visualizations, and peer‑reviewed publications as needed. Employment is at‑will and contingent upon grant funding.

• Lead acquisition, cleaning, and harmonization of secondary datasets from international cohort studies.
• Conduct data exploration and statistical analyses to extract insights from large, complex datasets.
• Unify different cognitive instruments; perform measurement in variance 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 data with principled methods such as MICE and IPW; quantify robustness.
• Maintain privacy‑conscious data handling (HIPAA/GDPR concepts).
• Maintain and analyze statistical models using best practices in machine learning and reproducible research workflows.
• Prepare publication‑ready tables, figures, and statistical summaries for interim and final reports.
• Develop statistical procedures and visualizations for specific research questions.
• Provide professional support to staff or faculty members in applying principles of data science.
• Build and analyze statistical models and reproducible data processing pipelines.
• Perform related work as needed.

Minimum:
College or university degree in a related field.

• College or university degree in a related field.
• 2–5 years of work experience in a related discipline.

• Graduate degree in Biostatistics, Statistics, Epidemiology, Psychometrics, Data Science, or related field.
• 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 in variance/IRT/score linking.
• Practical knowledge of missing data methods (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).

• Excellent written and oral communication.
• Organization and…