Senior Research Scientist

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.

The Team

As part of the Gates Foundation (GF), the Institute for Disease Modeling’s (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.

Your Role

We are seeking a full-time Senior Research Scientist with exceptional expertise in applied statistics, quasi-experimental research design, and quantitative research methods to join the Gender, Vulnerability, and Health Equity team at IDM. This individual will play a leading role in designing and executing cutting-edge research on the implementation and scale-up of health and development interventions, with a particular (but not exclusive) emphasis on women’s and children’s health. The candidate is expected to develop and apply statistical models aimed at understanding the social, economic and structural mechanisms which drive successful program implementation and expansion. They will be responsible for analyses focused around evaluating complex trade-offs that occur with programmatic and policy decisions (e.g., quasi-experimental, causal inference or optimization research).

What you’ll do:

• Lead the design and application of rigorous statistical models to understand the social, economic, and structural factors driving successful intervention or product implementation.
• Use quasi-experimental designs, causal inference techniques, and/or optimization methods to evaluate trade-offs in programmatic decisions.
• Analyze and integrate data from diverse sources—including household surveys, administrative data, as well as other nontraditional sources—often in contexts where data quality may vary.
• Study the interplay between population dynamics (e.g., demographics, household economics, socio-cultural norms, and behavior) and health system performance in shaping program effectiveness.
• Collaborate closely with interdisciplinary research teams, regional partners, and local organizations to co-design and implement research activities.
• Translate statistical findings into insights for policy and practice and communicate methods and results clearly to technical and non-technical audiences.
• Partner with other internal teams and external consultants to align research outputs with strategic priorities.
• Contribute to the scientific literature through peer-reviewed publications and thought leadership.
• Some international travel may be required.

Your Experience:

• A Ph.D. in public health, social science, or a related field with a strong foundation in advanced statistical methods, or in statistics, biostatistics, or applied mathematics with demonstrated application to real-world global health or development challenges.
• Minimum of five (5) years of post-PhD research experience, with demonstrated leadership in applied statistical research for global health or development.
• Proven expertise in advanced statistical methods such as causal inference, quasi-experimental designs, or optimization modeling.
• Strong coding and data analysis skills in at least one programming language (Python or R strongly preferred).
• Experience working with complex and messy datasets, including in low-resource or LMIC context