Technical Advisor, Analytics and Implementation Research

About the role

The Clinton Health Access Initiative (CHAI) is seeking a Technical Advisor for its Analytics and Implementation Research (AIR) team. The advisor works with program and country teams and government counterparts to conduct epidemiological analysis, model program impact and cost-effectiveness, monitor and evaluate ongoing efforts, and turn results into evidence for decision-makers. The role is open to candidates based in any CHAI program country, pending leadership approval and appropriate work authorization, and requires up to 15% travel.

Key responsibilities

• Apply quantitative and geospatial methods to help country teams identify high-priority programs, geographic targeting opportunities and health system efficiencies
• Build cost-effectiveness models to support evidence-based program decisions
• Manage analytical workstreams end-to-end, choosing the methodological approach and depth of analysis
• Use large-scale datasets (GBD, DHS, national surveys, IHME, implementation cost data) to inform program design and prioritization
• Collaborate across disease and geographic programs to validate analytical assumptions against on-the-ground realities
• Lead a portfolio of work, set long-term strategy, define priorities and workplans, and support the design of new pilot programs
• Pressure-test program theories of change by reviewing evidence, interrogating assumptions and assessing causal pathways and counterfactuals
• Prepare presentations, memos and briefing materials for senior leadership and major funders, and support engagement with funders
• Work with country teams and government partners to align interventions with national health systems

Required profile

• Postgraduate degree in epidemiology, economics or a related quantitative discipline
• 7+ years of experience applying quantitative methods to global health or development challenges, including cost-effectiveness analysis, impact evaluations and mathematical modeling
• Experience in cost-effectiveness modeling and evaluation design, including causal inference with observational data
• Experience assessing data quality across large survey and administrative datasets in multi-country settings
• Strong understanding of LMIC health systems, data limitations and the role of government and partners
• Ability to communicate research to donors, government stakeholders and other audiences, and to overlap working hours with US Eastern Time

Required skills

• Data management and statistical analysis, including Stata, R or SAS
• Cost-effectiveness modeling and evaluation design
• Fluency in using AI tools to synthesize information, with critical judgment on AI-generated work