• Owning metrics publishing quality and standards across data teams, ensuring datasets reliably power dashboards and client feeds • Partnering cross-functionally with Data, Engineering, and Product to define requirements, prioritize improvements, and drive adoption of best practices • Improving and scaling data platform workflows, from ingestion through transformation to delivery, enabling internal productivity and client value • Leveraging AI tools to enhance workflows, automate processes, and improve data quality and efficiency

• Review, tag, and classify retail product data (including product titles, descriptions, images, and attributes) to improve search relevance and product discoverability • Apply standardized guidelines and classification rules consistently across tasks • Identify data discrepancies, missing attributes, or ambiguous cases and escalate them to the team lead when necessary • Maintain high standards of data accuracy, completeness, and consistency while meeting defined quality thresholds and turnaround times • Perform quality checks by reviewing, correcting, or reprocessing data to ensure compliance with program requirements • Remove duplicate or incorrect entries to maintain a clean and reliable product catalog • Collaborate with team members and contribute to shared goals and continuous improvement initiatives • Perform additional related tasks as assigned to support product search and classification programs • Demonstrate flexibility by adapting to changes in guidelines, tools, or workflows

• Support 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 • Ensure 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 • Translate 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 • 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 considerations • Translate experimental results into clear, actionable recommendations to guide program and product decisions for individual country programs, and for the direction of GiveDirectly’s programming as a whole • 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, 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.

• Own the quality control process for daily media reports • Serve as a trusted point of contact for client feedback • Support special projects to improve customer delivery systems • Collaborate cross-functionally with product, success, and leadership teams • Take initiative on other high-priority assignments