Senior Global Health Development Economist

Join AIR as a Senior Global Health Development Economist/Data Scientist and use your experience and expertise to help us deliver on our mission: to generate and use rigorous evidence that contributes to a better, more equitable world for all. The Senior Global Health Economist /Data Scientist will lead complex research initiatives, design innovative analytical frameworks, guide client strategy, and contribute to AIR’s thought leadership in economics and public policy.

This position will sit within AIR’s International Development program , supporting a range of projects focused on data-driven decision-making, impact evaluation, predictive analytics, and health systems strengthenin g in low- and middle-income countries. The team works closely with major international donors , including the Gates Foundation, World Bank, U.S. Department of State, and UNICEF , to design and implement innovative analytical approaches that integrate survey, administrative, and geospatial data to inform policy and program design.

Candidates hired for the position may work remotely within the United States (U.S.) or from one of AIR's U.S office locations. This does not include U.S. territories.

About AIR:

Founded in 1946 and headquartered in Arlington, Virginia, the American Institutes for Research (AIR) is a nonpartisan, not-for-profit organization that conducts behavioral and social science research and delivers technical assistance to address some of the most pressing challenges in the United States and globally. We generate evidence and apply data-driven solutions that expand opportunities and improve lives for all.

Responsibilities

The responsibilities for the position include:

Serve as a bridge between data scientists, economists, and domain experts, translating across disciplines to ensure that analytical approaches are both methodologically rigorous and grounded in real-world development and global health challenges.

Design and implement quantitative analyses to address global health challenges in low and middle income countries , with a focus on predictive analytics , geospatial analyses, and causal design.

Apply machine learning and AI approaches, including predictive analytics, to real-world datasets (e.g., on AIR’s malaria early warning system and other geospatial or satellite data) to generate forward-looking insights and support innovative, data-driven solutions in global health and development contexts.

Develop and implement machine learning and predictive analytics workflows using Python (and, where appropriate, R) to analyze complex development and global health data.

Integrate and analyze survey, administrative, and remotely sensed (e.g., satellite or geospatial) data to generate novel insights on development and global health challenges.

Contribute to the design and execution of applied research studies, including experimental and quasi-experimental approaches where appropriate.

Translate complex analytical results into clear, actionable recommendations for policymakers, program implementers, and funders.

Collaborate with multidisciplinary teams, including economists, data scientists, and sector specialists, to integrate data-driven approaches into projects.

Support business development efforts, including drafting technical approaches, shaping analytical strategies for proposals, and contributing to engagement with donors such as the Gates Foundation, World Bank, U.S. Department of State, UNICEF, and other partners.

Engage with partners and stakeholders in Low- and Middle-Income Countries (LMICs) to ensure analyses are grounded in contextual realities and implementation constraints.

Mentor and supervise junior researchers and data scientists , fostering professional growth and technical excellence.

Manage multidisciplinary project teams and ensure high-quality deliverables aligned with client expectations.

Qualifications:

Education, Knowledge, and Experience

Ph.D. in Economics, Computer Science, Data Science, Public Policy, Global Health, or a related quantitative or social science field, with demonstrated expertise in econometrics, machine learning, computer science, applied statistics, or quantitative data science. A Master's degree in one of these fields, plus at least five years of relevant experience may be accepted in lieu of a Ph.D.

Minimum of 3 years of experience conducting quantitative research in policy-relevant domains.

Extensive experience with Python and R; working knowledge of Stata.

Proven track record of designing and executing data analytics tools, such as forecasting, geospatial analyses, Large Language Model (LLM) models, etc .

Prior research experience in areas such as development economics, health economics, global health, or international development more broadly.

Experience managing complex projects and working with government, nonprofit, or philanthropic client s.

Experience in business development and fundraising with nonprofit, government, or multilateral agen…