Data Analyst (Fraud Risk) - Internal Audit

GiveDirectly has delivered over $1B in cash directly to 2+ million people living in poverty across 15 countries since 2011. Their mission revolves around cash transfers as a scalable, cost-effective, and dignified form of aid, backed by extensive research.

This role is focused on protecting cash transfers from fraud, whether from external actors, field staff, or recipients. The Data Analyst will analyze operational, payments, and enrollment data to identify fraud risks, translate findings into actionable recommendations, and collaborate with product teams to build fraud-detection tools.

Core Responsibilities:

• Analyze fraud risk trends: Mine data to identify patterns like duplicate enrollments, collusion, identity fraud, and fund diversion across programs and geographies.
• Design and oversee fraud-risk indicators: Build, validate, and maintain recurring monitoring in partnership with data and operational teams.
• Identify data quality issues: Detect issues in current data and propose process improvements.
• Embed data-driven thinking: Ensure fraud-related decisions are data-driven and integrate this into Internal Audit processes.
• Translate findings into action: Package analysis into clear briefs and presentations for stakeholders with concrete recommendations.
• Support fraud investigations: Provide data pulls, quantify exposure, and reconstruct events for specific cases.
• Partner with Product and Central Data: Translate validated risk signals into product requirements and test controls.
• Partner with Data Engineering: Improve upstream data quality and pipeline improvements while owning the analytical layer.
• Strengthen methodology: Back-test risk indicators, monitor precision, and refine based on outcomes.
• Communicate uncertainty: Distinguish confirmed fraud from suspicious-but-unconfirmed patterns.

Requirements:

• 4+ years of experience in data analysis, audit, risk, or related roles; experience in internal audit, fraud/forensics, or nonprofit contexts is a plus.
• Strong SQL skills and comfort with large, messy datasets (e.g., Salesforce, payments, survey data).
• Python experience for reproducible analysis, automation, and data-quality testing is strongly preferred.
• Familiarity with analytical notebooks, version control, and documenting analysis for reproducibility.
• Experience with visualization tools (e.g., Looker, Tableau, Power BI) and/or Python/R for analysis.
• Ability to turn ambiguous analysis into clear narratives for non-technical stakeholders.
• Experience or interest in cross-functional collaboration with product and engineering teams.
• Sound judgment on evidence quality and transparent communication of findings.
• High integrity and discretion due to visibility into sensitive information.
• Alignment with GiveDirectly’s values, prioritizing recipient wellbeing.