Data Analyst, Internal Audit

Location: Remote, with a requirement to overlap with an East Africa timezone by at least 3 hours. We can only employ people in certain places at the moment, so it's worth checking our current list of eligible countries to see if yours is included. If you're based elsewhere, it's not an automatic no, but do flag it early with your recruiter. We are unable to sponsor or take over sponsorship of employment visas in the U.S. or U.K. at this time.

GD moves cash directly into the hands of people living in poverty, often in contexts with limited infrastructure and oversight. Protecting that cash from fraud — whether attempted by external bad actors, field staff, or recipients — is core to our mission and to donor trust. Internal Audit is GD's second line of defense against fraud, and this role sits at the center of it.

We're looking for a Data Analyst who can turn raw operational, payments, and enrollment data into a clear picture of where fraud risk is emerging across our programs, translate those patterns into recommendations that change how projects are designed, and partner with our Product team to build the tools that help us catch fraud earlier and more reliably. You'll move fluidly between deep analysis, clear storytelling to stakeholders who aren't data specialists, and hands-on collaboration with engineers and product managers.

This role sits within Internal Audit but has a dotted reporting line to the Director of Data in order to ensure we meet the organisational technical quality bar and standardise analytical and engineering practises and ways of working across the organisation. Internal Audit owns fraud-risk questions, indicator definitions, investigative analysis, and interpretation, while the central Data team owns shared data infrastructure, production pipelines, governed data models, and platform standards. The analyst bridges the two: contributing domain requirements, validation, and quality assurance, and ensuring this work aligns with organization-wide analytical and engineering practices. 

You are as comfortable in a spreadsheet or SQL console as you are in a room with country program leads explaining why a trend matters and what to do about it. You default to evidence over instinct, but you know that a good insight is only useful if someone acts on it.

• 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.

• 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.

At GiveDirectly, we strive to pay our employees generously and equitably. We use an accredited third party salary aggregator to calculate what we believe to be competitive pay based on role, location, and cost of living. We also have a no negotiation policy to ensure we are paying staff equitably across roles. Read more about our compensation philosophy here.

Unless otherwise noted, the benefits stipend may be used to cover benefits or taken as additional taxable income.

United States

• Base Salary: $107,070
• Bonus at Target Performance: 10% (~$10,707, with potential for upside. For reference, with the organization’s current performance multiplier, this amount would be $12,527 in 2025)
• Estimated Total Compensation at Target: $117,777
• Annual Benefits Stipend: $21,393

United Kingdom

• Base Salary: £70,250
• Bonus at Target Performance: 10% (~£7,025, with potential for upside. For reference, with the organization’s current performance multiplier, this amount would be £8,219 in 2025)
• Estimated Total Compensation at Target: £77,275
• Annual Benefits Stipend: £2,760

Kenya

• Base Salary: $72,550
• Bonus at Target Performance: 10% (~$7,255, with potential for upside. For reference, with the organization’s current performance multiplier, this amount would be $8,488 in 2025)
• Estimated Total Compensation at Target: $79,805
• Annual Benefits Stipend: $0

This role is fully remote, so if you are not based in the US, UK or Kenya, we will share an estimated salary benchmark for the country you are based in during the hiring process. 

At GiveDirectly, we work to ensure that you have everything you need to excel in your role and on your team, including:

• 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

 #LI-REMOTE