Research Scientist - Aging and Disability Economics, Evaluation, and Public Health Surveillance
Listed on 2026-07-09
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Research/Development
Public Health, Data Scientist, Research Analyst -
Healthcare
Public Health, Data Scientist
Job Summary
NORC's Public Health Analytics Program conducts research and surveillance on age‑related chronic disease and disability. The program's portfolio includes the Dementia Data Hub (Dementia Data Hub .org), the Vision and Eye Health Surveillance System (VEHSS), Sound Check Map , fall prevention research, and additional projects on aging‑related conditions. The work is funded through NIH grants, federal contracts, and foundation support, and is conducted in partnership with academic institutions, federal agencies, and industry collaborators.
The program uses Medicare claims, national surveys, and other secondary data to measure disease burden, identify disparities, and inform policy.
We are hiring a Research Scientist to play a central analytic role across this portfolio. The role suits an early‑career PhD scientist who wants to publish, build a research portfolio, and contribute to a long‑running, well‑funded program of work.
Work LocationThis is a hybrid role based in one of our office locations:
Chicago Loop, Washington, DC, Atlanta, GA, or Cambridge, MA. Qualified applicants must be eligible to work in the U.S.; visa sponsorship is not available.
The Public Health Research team conducts work on a variety of public health topics including disease surveillance, health communication science, health promotion, interpersonal conflict and violence, rural health, and social determinants of health. The team includes health policy experts, nationally recognized researchers, skilled methodologists, and leaders in survey design and implementation.
Responsibilities- Lead analyses of Medicare and Medicaid Fee‑for‑Service claims using the CMS Virtual Research Data Center, including work with the Master Beneficiary Summary File, Inpatient, Outpatient, Carrier, SNF, Home Health, Hospice, DME, and Part D files.
- Develop, validate, and refine claims‑based case definitions and ascertainment models for dementia, vision impairment, hearing loss, falls, and related conditions.
- Conduct analyses using nationally representative longitudinal cohort studies and their linkages to Medicare claims.
- First‑author peer‑reviewed publications under PI mentorship and co‑author across the team's portfolio.
- Contribute to NIH grant applications, federal contract deliverables, foundation‑supported research, and pilot grant program activities.
- Produce analytic code that is reproducible, auditable, and built to survive verification.
- PhD in economics, health services research, public policy, epidemiology, or a closely related quantitative field; recent post‑doc or assistant professor equivalent experience preferred.
- Demonstrated proficiency in SAS, R, or Python for analysis of large administrative claims and survey datasets. SAS is preferred; strong R or Python candidates will be considered. STATA and SPSS proficiency alone is not sufficient.
- Direct, hands‑on experience with Medicare claims data or comparable large administrative health datasets.
- A track record of scientific writing and the ability to first‑author publications under mentorship.
- Meticulous attention to detail and a strong instinct for when something is not quite right in the data or the analysis.
- Qualified applicants must be eligible to work in the U.S.; visa sponsorship is not available.
- Expertise in dementia measurement, regardless of data source.
- Working knowledge of nationally representative longitudinal cohort studies relevant to aging, such as the Health and Retirement Study (HRS), the National Health and Aging Trends Study (NHATS), or the National Social Life, Health, and Aging Project (NSHAP), including their wave structure, imputation conventions, and Medicare linkages.
- Experience working in the NIA LINKAGE data enclave.
- Experience working in the CMS Virtual Research Data Center, including familiarity with DUA requirements and CMS cell suppression rules.
- Experience with epidemiological estimation, machine learning, Bayesian regression methods, and econometrics.
- First‑author peer‑reviewed publications in health services research, epidemiology, or aging.
- Sustained research funding through NIH grants, federal…
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