M&E Associate - Rapid Experimentation (Remote)

About GiveDirectly

Since 2011, GiveDirectly has proven that direct cash transfer is an effective way to fight poverty at scale. We’ve delivered more than $1B in cash directly to 2+ million people living in poverty and crisis across 15 countries. We’re growing fast with expectations to hit $1B in revenue in 2026.

About this role

The Monitoring and Evaluation (M&E) Associate for Rapid Experimentation will support the execution and analysis of A/B tests across GiveDirectly’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, then uses those learnings to refine and improve programs in real time.

What you’ll do:

Execute and manage A/B tests across programs

• 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

Ensure accurate measurement and high-quality data for experiments

• 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

Analyze experimental data and interpret results to inform decisions

• 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 considerations

Translate results into actionable insights and learning across experiments

• Translate experimental results into clear, actionable recommendations to guide program and product decisions
• Structure results and key learnings so they can be reused to inform future experiments and program design
• Prepare concise learning products (e.g., memos, summaries) that serve multiple audiences, including internal Programs and Product teams, and external academic partners
• Prepare and clean de-identified datasets for sharing with external PIs and academic partners
• Contribute to cross-country discussions to ensure learnings from experiments are shared and applied across contexts

What you’ll bring:

• Exceptional alignment with GiveDirectly Values and active demonstration of core competencies: emotional intelligence, problem-solving, project management, follow-through, and fostering inclusivity
• Bachelor’s degree (or equivalent) in Economics, Statistics, Public Policy, or a related quantitative field
• 2–4 years of experience working with data in applied settings (e.g., experimentation, evaluation, analytics, or program learning), ideally in development, tech, or operations-focused roles
• Solid understanding of experimental design and causal inference concepts (e.g., randomization, treatment/control groups, units of randomization, statistical power, bias) and how to apply them in real-world program contexts
• Experience using R, Python, or Stata to clean, manipulate, and analyze data, including working with multiple data sources (e.g., survey or administrative data)
• Experience collaborating with cross-functional teams (e.g., programs/operations, product, or research) and external partners to implement projects and solve problems
• Fluency in English required
• Comfort working at pace—able to manage multiple workstreams simultaneously, make progress with imperfect data, and iterate quickly based on emerging findings
• Ability to interpret results beyond statistical significance and communicate clear, actionable insights to both technical and non-technical audiences

Reports to: Senior Manager, Monitoring and Evaluation

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%).

About GiveDirectly

Since 2011, GiveDirectly has proven that direct cash transfer is an effective way to fight poverty at scale. We’ve delivered more than $1B in cash directly to 2+ million people living in poverty and crisis across 15 countries. We’re growing fast with expectations to hit $1B in revenue in 2026.

About this role

The Monitoring and Evaluation (M&E) Associate for Rapid Experimentation will support the execution and analysis of A/B tests across GiveDirectly’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, then uses those learnings to refine and improve programs in real time.

What you’ll do:

Execute and manage A/B tests across programs

• 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

Ensure accurate measurement and high-quality data for experiments

• 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

Analyze experimental data and interpret results to inform decisions

• 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 considerations

Translate results into actionable insights and learning across experiments

• Translate experimental results into clear, actionable recommendations to guide program and product decisions
• Structure results and key learnings so they can be reused to inform future experiments and program design
• Prepare concise learning products (e.g., memos, summaries) that serve multiple audiences, including internal Programs and Product teams, and external academic partners
• Prepare and clean de-identified datasets for sharing with external PIs and academic partners
• Contribute to cross-country discussions to ensure learnings from experiments are shared and applied across contexts

What you’ll bring:

• Exceptional alignment with GiveDirectly Values and active demonstration of core competencies: emotional intelligence, problem-solving, project management, follow-through, and fostering inclusivity
• Bachelor’s degree (or equivalent) in Economics, Statistics, Public Policy, or a related quantitative field
• 2–4 years of experience working with data in applied settings (e.g., experimentation, evaluation, analytics, or program learning), ideally in development, tech, or operations-focused roles
• Solid understanding of experimental design and causal inference concepts (e.g., randomization, treatment/control groups, units of randomization, statistical power, bias) and how to apply them in real-world program contexts
• Experience using R, Python, or Stata to clean, manipulate, and analyze data, including working with multiple data sources (e.g., survey or administrative data)
• Experience collaborating with cross-functional teams (e.g., programs/operations, product, or research) and external partners to implement projects and solve problems
• Fluency in English required
• Comfort working at pace—able to manage multiple workstreams simultaneously, make progress with imperfect data, and iterate quickly based on emerging findings
• Ability to interpret results beyond statistical significance and communicate clear, actionable insights to both technical and non-technical audiences

Reports to: Senior Manager, Monitoring and Evaluation

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%).