Data Analyst, Internal Audit

What you’ll do

• Analyze fraud risk trends: Mine enrollment, payment, survey, and field-operations data to identify patterns, anomalies, and emerging fraud typologies (e.g., duplicate enrollments, collusion, identity fraud, diversion of funds) across GD’s programs and geographies.
• Design and oversee fraud-risk indicators: Build, validate, and maintain recurring monitoring in partnership with central data and operational control owners.
• Identify data quality issues in data currently collected by Internal Audit, and help design and implement process improvements to resolve them.
• Embed data-driven thinking into IA design: Identify fraud-related decisions where GiveDirectly should be more data-driven, and incorporate this into Internal Audit process design.
• Translate findings into action: Package analysis into clear, credible briefs and presentations for country teams, operations leadership, and senior management, with concrete recommendations for reducing fraud risk.
• Support fraud investigations: Provide data pulls, quantify exposure, and help reconstruct events for specific cases — ensuring analysis is reproducible and audit-ready, with documented queries, data lineage, assumptions, exclusions, and QA checks.
• Partner with Product and Central Data on fraud-detection tooling: Translate validated risk signals into product requirements, contribute domain expertise and acceptance criteria, and test whether controls operate as intended — while maintaining clear ownership and independence boundaries.
• Partner with Data Engineering on upstream data quality and pipeline improvements, while owning the downstream analytical layer.
• Strengthen methodology over time: Back-test risk indicators against investigated cases, monitor precision, coverage, and false-positive rates, and refine based on outcomes and emerging risks.
• Communicate uncertainty honestly: Distinguish confirmed fraud from suspicious-but-unconfirmed patterns, helping stakeholders make decisions without overstating findings.

What you’ll bring

• 4+ years of experience in data analysis, audit, risk, or a related analytical role; experience in internal audit, fraud/forensics, or a nonprofit/development context is a plus but not required.
• Strong SQL skills and comfort working directly in large, sometimes messy operational datasets (Salesforce data, payments data, survey data).
• Experience using Python for reproducible analysis, automation, record linkage, anomaly investigation, or data-quality testing is strongly preferred.
• Familiarity with analytical notebooks, version control, and documenting analysis so it can be reviewed and reproduced by others is a plus.
• Experience with visualization tools (e.g., Looker, Tableau, Power BI) and/or Python/R for analysis.
• Demonstrated ability to turn ambiguous, exploratory analysis into a clear, defensible narrative for non-technical stakeholders.
• Experience or strong interest in working cross-functionally with product and engineering teams to translate analytical needs into product requirements.
• Sound judgment on evidence quality: knowing the difference between a real signal and noise, and communicating that distinction transparently.
• High integrity and discretion — this role will have visibility into sensitive information about staff, recipients, and active investigations.
• Alignment with GiveDirectly’s values, including prioritizing recipient wellbeing above all else.
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