Senior Research Scientist, Global Health Modeling (*LTE

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 includes 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.

We’re committed to creating an environment where you can thrive both personally and professionally.

The Institute for Disease Modeling (IDM) is part of the Gates Foundation (GF) and supports 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 defined by innovation and collaboration, with frequent collaboration with international health agencies, ministries of health, universities and research institutes worldwide.

Senior Research Scientist – Gender, Vulnerability and Health Equity (GVHE) research team. This full‑time role requires deep expertise in applied statistics for population health research. The position is a limited‑term contract for 2 years with relocation provided.

• Design and implement applied statistical analyses addressing population level questions related to demography, socioeconomic dynamics, and public health outcomes.
• Develop and apply forecasting and time‑series models to inform planning, scenario analysis, and strategic decision‑making.
• Analyze complex, real‑world datasets (surveys, surveillance systems, administrative and programmatic data) that often contain 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 & 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 outputs, including internal reports, policy briefs, and peer‑reviewed publications.
• Support reproducible research practices through well‑documented, maintainable code and analytical workflows.
• May require international travel.

• PhD in statistics, biostatistics or related quantitative discipline (e.g., mathematical demography, economics, data science, etc.).
• Minimum of five (5) years post‑PhD experience conducting applied statistical research in public health or population‑level research settings.
• Demonstrated experience applying statistical methods to demographic, socioeconomic, and health‑related research questions.
• Strong expertise in forecasting and time‑series analysis, including model validation, uncertainty quantification, and scenario‑based analysis.
• Advanced skills in Python and R, with experience developing reproducible and scalable analytical workflows.
• Proven ability to work with messy, incomplete, and imperfect real‑world data.
• Experience collaborating in interdisciplinary and cross‑sector research environments.

• Strong written and verbal communication skills, with the ability to explain statistical findings clearly to diverse audiences.
• Experience working in global health…