M&E Associate - Rapid Experimentation; Remote

Give Directly has delivered more than $1B in cash directly to 2+ million people living in poverty across 15 countries since 2011. We believe cash transfers are one of the most scalable, cost-effective, and dignified forms of aid, with the research to back it up. Our work has been covered by The Economist, NPR, TED, and The Washington Post. We are one of Time
100’s Most Influential Companies of 2026.

Our culture is candid, analytical, and non-hierarchical. We support high ownership and real professional growth. Curious about what it’s really like to work here? Read our values and hear from the people who do. If they resonate, this could be a great fit!

Priority Final date to receive applications: May 15, 2026

The Monitoring and Evaluation (M&E) Associate for Rapid Experimentation will support the execution and analysis of A/B tests across Give Directly’s programs, contributing to the use of data to inform program and product decisions. This role is fast-paced and iterative - the team runs frequent, lightweight experiments designed to generate actionable answers quickly, and then uses those learnings to refine and improve programs in real time.

This includes:

• Supporting the execution and analysis of rapid A/B tests across Give Directly projects , ensuring that experiments are implemented effectively and contribute to learning within and across projects
• Ensuring experiments are implemented correctly, measured reliably, and analyzed to produce clear and credible results , working closely with Programs, Product, Research teams, and external academic partners to align experimental design with program delivery, data collection, and operational realities
• Translating findings into actionable insights and well-structured learning products that serve multiple audiences - internal program and product teams, and external academic partners, ensuring results inform both immediate decisions and longer-term program direction

In practice, this role is about managing a high volume of rapid experiments and turning results into clear, credible evidence that continuously shapes how Give Directly’s programs are refined and improved.

Reports to: Senior Manager, Monitoring and Evaluation

Level :
Associate

Travel Requirement: This role is based in Rwanda. Travel within East Africa is a regular part of the role, estimated at up to 25% of the time, to support field operations across country programs. Additional international travel may occur for trainings or team convenings, as needed (estimated up to 10%)

• Conduct power calculations to ensure experiments are both statistically rigorous and feasible to implement within program constraints
• Set up pre-specified experimental designs, applying defined experimental groups, outcome measures, and measurement timelines
• Review experiment setups prior to launch and flag execution and measurement risks that may affect interpretability
• Ensure experiments are well-coordinated and executed as designed, aligning implementation with research plans and integrating smoothly into program delivery across Programs and Product teams
• Work at a fast pace across a portfolio of 2–3 live A/B tests at any given time, designed to generate actionable answers quickly and feed rapid iteration of programs and products

• Collaborate with external Principal Investigators (PIs) to ensure measurement approaches and data collection are aligned with research design and implementation realities
• Ensure experimental outcomes are captured accurately and consistently by applying established indicator and measurement approaches
• Prepare and manage datasets that are clean, well-structured, and ready for analysis using survey, administrative, and product data
• Identify and flag data quality risks (e.g., missingness, inconsistencies, measurement error) that could affect the validity of experimental conclusions
• Conduct targeted literature reviews to ensure measurement approaches are grounded in evidence and aligned with best practices

• Generate reliable and decision-ready analyses of experimental data from A/B tests
• Assess the magnitude and direction of effects and highlight what the results do and do not suggest, noting key limitations
• Ensure results are clearly understood and appropriately interpreted, given data quality, sample size, and implementation…