Senior Global Health Development Economist
Summary
Senior economist/data scientist designs and leads global health research using predictive analytics, geospatial data, and machine learning to inform policy in low- and middle-income countries.
Join AIR as a Senior Global Health Development Economist 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 strengthening 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.
- 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 clients.
- Experience in business development and fundraising with nonprofit, government, or multilateral agencies in the global health space.
Skills
- Exceptional quantitative and analytical skills.
- Strong writing and communication skills, with the ability to translate technical findings for non-technical audiences.
- Excellent organizational and time management skills.
- Strong business development acumen and proposal writing experience.
- Ability to lead teams, manage multiple priorities, and meet deadlines in a fast-paced environment.
- Effective collaborator in virtual and cross-functional team settings.
Disclosures: Applicants must be currently authorized to work in the U.S. on a full-time basis. Employment-based visa sponsorship (including H-1B sponsorship) is not available for this position. Depending on project work, qualified candidates may need to meet certain residency requirements.
American Institutes for Research is an equal employment opportunity/affirmative action employer. All qualified applicants will receive consideration for employment without discrimination on the basis of any characteristic protected by applicable federal, state or local law, including, but not limited to, actual or perceived race (including traits historically associated with race, such as hair texture, hair type, and protective hairstyles such as braid, locks, and twists), creed, color, religion, alienage or national origin, ancestry, citizenship status, age, physical or mental disability, medical condition (e.g., cancer), sex, gender, gender identity or expression (including transgender status), sexual orientation, marital status, civil union status, pregnancy, childbirth or related medical conditions, genetic information, or military or veteran status. AIR adheres to strict child safeguarding principles. All selected candidates will be expected to adhere to these standards and principles and will therefore undergo reference and background checks. AIR maintains a drug-free work environment.
ACCESSIBILITY NOTICE: If you need a reasonable accommodation for any part of the employment process due to a physical or mental disability, please send an email to Taliba Boone at tboone@air.org or call 202.403.5000.
Fraudulent Job Scams Warning & Disclaimer: AIR is aware of individuals falsely presenting themselves as AIR representatives. Fraudulent job scams seek to extract sensitive information or money from victims. To protect yourself, please be aware that AIR recruitment will only email you from an “@air.org” domain. Please take extra caution while examining the email address, for example jdoe@air.org is correct and jdoe@aircareers.org is not a legitimate AIR email address. If you are unsure of the legitimacy of a communication you have received, please reach out to recruitment@air.org. If you see a job scam, or lose money to one, report it to the Federal Trade Commission (FTC) at ReportFraud.ftc.gov. You can also report it to your state attorney general. Find out more about how to avoid scams atftc.gov/scams.
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AIR’s Total Rewards Program, is designed to reward our staff competitively and motivate them to achieve our critical mission. This position offers the anticipated annual salary as listed. Salary offers are made based on internal equity within the institution and external equity with competitive markets. Please note this is the annual salary range for candidates that are based in the United States.
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