Senior Research Scientist, Population Health Modeling (Nutrition and Disease) (*2-year LTE

The Foundation

We are the largest nonprofit fighting poverty, disease, and inequity around the world. Founded on a simple premise: people everywhere, regardless of identity or circumstances, should have the chance to live healthy, productive lives. We believe our employees should reflect the rich diversity of the global populations we aim to serve. We provide an exceptional benefits package to employees and their families which include comprehensive medical, dental, and vision coverage with no premiums, generous paid time off, paid family leave, foundation-paid retirement contribution, regional holidays, and opportunities to engage in several employee communities.

As a workplace, we’re committed to creating an environment for you to thrive both personally and professionally.

As part of the Gates Foundation (GF), the Institute for Disease Modeling (IDM) mission is to support global efforts to eradicate infectious diseases and achieve permanent improvements in health by developing, using, and sharing computational modeling tools and promoting quantitative decision‑making. The IDM team is composed of research scientists and software developers who create advanced models of disease transmission, develop computational tools to inform global disease eradication policy, conduct analysis of epidemiologically‑and policy‑relevant data, and identify and address critical knowledge gaps.

IDM is a highly dynamic organization with a work environment that is defined by innovation and collaboration. As part of our work, we routinely collaborate with international health agencies, ministries of health in the developing world, as well as universities and research institutes across the globe.

* This position is a limited-term position for 2 years. Relocation will be provided.

In this role, we seek a full‑time Senior Research Scientist to join IDM’s Gender, Vulnerability and Health Equity (GVHE) research team with deep expertise in applied statistics for population health research. This role sits at the intersection of nutrition science and population health modeling: the core question is how nutrition investments interact with and amplify outcomes across infectious disease, maternal health, and human capital programs.

The successful candidate will bring enough depth in nutrition science to identify which interventions are likely to produce synergistic effects and to guide the disease modeling work, while also having strong quantitative skills in causal inference and comparative impact estimation across domains. This role is well suited to a quantitative epidemiologist or global health researcher who has worked across disease areas and has meaningful nutrition exposure.

Candidates whose primary background is in mechanistic or biological systems modeling of nutrition, single‑domain health economics without cross‑program experience, or nutrition epidemiology without a modeling or investment‑decision context are unlikely to be a good fit.

• Model how nutrition investments interact with and modify outcomes in infectious disease, maternal health, and related domains — developing stratified population models that make effect heterogeneity by nutritional status, disease burden, and geography empirically visible
• Translate multi‑intervention impact estimates into comparative evidence that informs portfolio‑level decisions
• Analyze complex, real‑world datasets (e.g., surveys, surveillance systems, administrative and programmatic data), often characterized by missingness, bias, or measurement limitations
• Apply advanced modeling methods to extrapolate evidence on program/ intervention effectiveness to different contexts and populations, generating rigorous projections to inform program scale‑up and future investment planning
• Quantify and communicate assumptions, uncertainty, and limitations of analyses to both technical and non‑technical audiences
• Collaborate closely with interdisciplinary teams to co‑develop research questions and analytical approaches
• Translate statistical results into clear, actionable insights for internal stakeholders and external partners
• Contribute to high‑quality applied research…