Role Description 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, 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 GiveDirectly 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 GiveDirectly’s programs are refined and improved. Qualifications - 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. Requirements - 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, designed to generate actionable answers quickly and feed rapid iteration of programs and products. - 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. - Prepare concise learning products (e.g., memos, summaries) that serve multiple audiences, including internal Programs and Product teams, and external academic partners, ensuring findings are clearly communicated for both technical and non-technical stakeholders. - Prepare and clean de-identified datasets for sharing with external PIs and academic partners, ensuring data is structured, well-documented, and ready for independent analysis. - Contribute to cross-country discussions to ensure learnings from experiments are shared and applied across contexts, and maintain clear documentation so experiments are easy to track, understand, and build on over time. Benefits - A supportive team that works hard and cares hard. - A robust health benefits plan (exact details will vary by country). - Flexible paid time off that staff is encouraged to take. - Allowances for desk set-up and learning and development.

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