[Hiring] Research Associate, Analytics and Implementation Research @Clinton Health Access Initiative

Role Description

CHAI is seeking a highly motivated individual with public health experience and analytical skills to work with CHAI’s program and country teams and government counterparts to conduct epidemiological analysis, model program impact and cost-effectiveness, and monitor and evaluate the success of ongoing efforts. The Research Associate will be responsible for:

• Developing and refining cost-effectiveness analyses for candidate programs across CHAI's portfolio.
• Conducting rigorous pressure-tests of program theories of change by critically reviewing literature.
• Applying quantitative methods to help CHAI country teams identify priority programs, efficiencies, and geographic targeting opportunities.

This role is a unique opportunity to apply advanced epidemiological and quantitative analytics to high-impact health interventions in some of the world's most challenging contexts. You will have the chance to work across CHAI's diverse health programs, contribute to complex analytical work, and directly influence how resources are allocated to save lives at scale.

This role is open to candidates based in any CHAI program country, pending leadership approval and appropriate work authorization. Candidates should be able to accommodate overlapping working hours with US Eastern Time (ET) to facilitate team collaboration.

Qualifications

• Postgraduate degree in epidemiology, economics, or related quantitative discipline.
• 3+ years of experience applying quantitative methods to global health or development challenges, including cost-effectiveness analysis, impact evaluations, and mathematical modeling.

Requirements

• Exceptional analytical and critical thinking skills, with proven capacity to pressure test complex theories of change.
• Excellent skills in data management and statistical analysis, including use of software such as Stata, R, or SAS.
• Experience in cost-effectiveness modeling and evaluation design (including causal inference using observational data), tailoring methodologies to diverse contexts to generate actionable insights.
• Experience assessing and reconciling data quality issues across large survey and administrative datasets, particularly in multi-country settings.
• Strong understanding of the LMIC health systems context, including data limitations, operational constraints, and the role of government and partner ecosystems.
• Strong communication skills, including the ability to present complex quantitative findings to donors, government stakeholders, and other diverse audiences.
• Demonstrated fluency in using AI tools to synthesize information and produce high-quality analytical outputs, paired with the judgment to critically evaluate AI-generated work.
• Proven ability to work independently in a fast-paced environment.
• Commitment to collaborative work with a remote, geographically dispersed team across multiple time zones.
• Fluency in English.

Preferred

• Experience using geospatial methods to support budget optimization, program targeting, or subnational prioritization analyses is an advantage.
• Proficiency in one or more languages spoken in CHAI countries.