Outcome: Supported evidence-based allocation of approximately $80M in annual global health funding by evaluating program performance, tradeoffs, and equity across countries under severe budget and data constraints.

Decision Context

• Funding decisions carried direct consequences for population health outcomes
• Resources were limited relative to need, requiring defensible prioritization
• Decision-makers needed transparent, auditable analytics to support interagency consensus

What I Led

• Performance modeling: Designed econometric analyses to assess program effectiveness and marginal returns
• Comparative evaluation: Quantified tradeoffs across regions and countries to support allocation decisions
• Decision support: Translated complex analysis into concise briefings used in budget and policy discussions
• Automation & reliability: Built standardized R and Python pipelines, reducing turnaround from hours to seconds while improving auditability
• Secure analytics: Worked in security-cleared environments with sensitive data and strict governance requirements

Impact

• Enabled consistent, repeatable evaluation of program performance across countries
• Improved transparency and defensibility of high-stakes funding decisions
• Reduced analyst burden while increasing confidence in reported results

This project illustrates how rigorous analytics can guide capital allocation decisions when uncertainty, equity considerations, and operational constraints intersect.