M&E Associate - Rapid Experimentation
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GiveDirectly is seeking an M&E Associate in Nairobi to execute and analyze A/B tests across its programs. The full-time role supports rapid experimentation, rigorous measurement, and data-driven improvements to programs and products.
- Remote position based in Kenya
- Manage 2–3 live A/B tests across programs
- Work from 8:00 to 5:00
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GiveDirectly is seeking an M&E Associate in Nairobi to execute and analyze A/B tests across its programs. The full-time role supports rapid experimentation, rigorous measurement, and data-driven improvements to programs and products.
- Remote position based in Kenya
- Manage 2–3 live A/B tests across programs
- Work from 8:00 to 5:00
- Use survey, administrative, and product data
- Collaborate with Programs, Product, and external researchers
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- RequiredAvailability to work in NairobiThe vacancy is associated with this location.
- UnclearComfort with the Full Time contract termsConfirm hours, duration, probation and benefits at the original source.
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Original source description
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M&E Associate - Rapid Experimentation (Remote)
GiveDirectly, Inc
Research, Teaching & Training
Today
New
Nairobi
Full Time
Confidential
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Language Requirement:
English
Work Type:
Full Time
Working Hours:
8 to 5
Applicant Location:
Kenya
Job descriptions & requirements
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%).
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