Associate Program Manager

AidData's TUFF (Tracking Underreported Financial Flows) project is a rigorous effort to document the true scale of China's overseas lending and grant-making — spanning 217+ countries, $2.2 trillion in financial transactions, and 33,000+ records. As an Associate Program Manager, you'll be at the center of that work, contributing to a dataset that researchers, journalists, and policymakers rely on to understand the scope and terms of China's overseas financing.

This is a role for someone who is energized by detail-oriented researcher that has real policy stakes — and who wants to build expertise at the cutting edge of development finance transparency.

What you'll do:

• Supervise and mentor student research teams coding China's loans, grants, and debt relief projects across low- and middle-income countries
• Run quality assurance processes on TUFF dataset outputs, utilizing data analysis software to run quality assurance
• Carry out complex data collection and categorization tasks — tracking down primary source records on Chinese loans and grants, extracting key financial terms, and applying TUFF coding standards to ambiguous or incomplete project documentation
• Pilot and evaluate AI-enabled workflows to support data collection, coding, and quality assurance across the TUFF research pipeline
• Contribute to methodology and dataset updates to reflect evolving nature of Chinese official financing and improve end-user experience
• Contribute labor estimates and methodology inputs to grant proposals, supporting AidData's pipeline of externally-funded research

Required Qualifications: 

• Bachelor’s Degree or equivalent combination of education/experience.
• Basic knowledge of research methods in either qualitative and/or quantitative research.
• Demonstrated ability to use desktop publishing, word processing, spreadsheet and database applications, etc.
• Basic knowledge of data management, mixed methods/qualitative research methods, and/or analytical software (e.g. Excel, R, Tableau, SQL).

Preferred Qualifications: 

• Experience in AidData’s TUFF coding methodology.
• Professional or academic experience in developing countries.
• Experience in global development, China studies, finance, or economics. 
• Ability to conduct research in Mandarin, Spanish, French, or other non-English languages