[![Image]() Meet Jobicy Copilot — free AI autofill for job applications + remote job alerts ›](#) [All remote jobs](https://jobicy.com/jobs.md)Open role[![GiveDirectly logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/06/07307149-221.png)](https://jobicy.com/company/givedirectly.md)Remote opportunity at[GiveDirectly](https://jobicy.com/company/givedirectly.md) # Data Analyst, Internal Audit Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/givedirectly.md)Share12 Sep 2026Published43Listing views3Application actions12 Oct 2026Apply before Opportunity details ## About this role. AI SummaryGiveDirectly seeks a Data Analyst within Internal Audit to detect, investigate, and reduce fraud risk across cash-transfer programs. The role combines SQL and Python analysis of enrollment, payment, survey, and operational data with the design and validation of recurring fraud-risk indicators. The analyst will produce audit-ready, reproducible work and translate findings into practical recommendations for country teams and senior leadership. They will also partner with Product, Data Engineering, and the central Data team to improve fraud-detection tools, data quality, and monitoring pipelines. Success requires strong analytical judgment, discretion with sensitive data, and clear communication of uncertainty to non-technical stakeholders. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 4/5EasyHard ### Pace & Pressure 4/5RelaxedFast-paced ### Autonomy Level 4/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThis is a technically and analytically demanding role because it requires identifying weak fraud signals in messy, high-stakes operational data and validating them against real investigations. The analyst must independently balance audit rigor, reproducibility, data-quality constraints, and cross-functional delivery. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job medianMarket rate$89,810US market range$85k–$125k0$138k AI insightThe disclosed USD annual base salaries are $72,550 for Kenya and $107,070 for the United States, producing a USD offer midpoint of $89,810. The UK base salary is separately disclosed as £70,250 and is excluded from the USD calculation. For the US market, an estimated annual base-pay range for a mid-level data analyst specializing in internal audit, fraud analytics, and SQL/Python is approximately $85,000–$125,000, varying by location and depth of forensic or risk experience. ## Core skills Skills and capabilities most closely associated with this opportunity. [SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[Fraud Analytics](https://jobicy.com/jobs?search_keywords=Fraud%20Analytics.md)[Internal Audit](https://jobicy.com/jobs?search_keywords=Internal%20Audit.md)[Risk Assessment](https://jobicy.com/jobs?search_keywords=Risk%20Assessment.md)[Data Quality](https://jobicy.com/jobs?search_keywords=Data%20Quality.md)[Anomaly Detection](https://jobicy.com/jobs?search_keywords=Anomaly%20Detection.md)[Data Visualization](https://jobicy.com/jobs?search_keywords=Data%20Visualization.md)[Stakeholder Communication](https://jobicy.com/jobs?search_keywords=Stakeholder%20Communication.md) Sample interview questionsDescribe how you would identify potential duplicate or fraudulent enrollments in a large program dataset.I would first define the relevant risk hypotheses and available identifiers, such as names, phone numbers, device IDs, locations, household attributes, and payment accounts. Using SQL and Python, I would profile data quality, create exact and fuzzy matching rules, and score suspicious records while documenting assumptions and exclusions. I would validate results against known investigation outcomes where available, quantify false positives, and present prioritized cases with clear evidence rather than treating every anomaly as confirmed fraud. How would you make a fraud-risk analysis reproducible and audit-ready? I would use version-controlled queries and notebooks, preserve source references and data extracts where permitted, and document transformations, assumptions, exclusions, validation checks, and limitations. I would structure outputs so another analyst can rerun the analysis and trace each conclusion back to governed data sources. Before sharing findings, I would perform peer review or QA checks and clearly distinguish observations, inferences, and confirmed cases. Tell us how you would communicate an uncertain fraud signal to non-technical leadership. I would begin with the operational question and explain the size and pattern of the signal in plain language. I would state the evidence, confidence level, alternative explanations, and the limits of the data, avoiding language that labels a pattern as fraud before it is confirmed. I would then recommend proportionate next steps, such as targeted review, additional controls, or a pilot, and explain how we would measure whether the action works. How would you work with Product and Data Engineering to operationalize a validated risk indicator? I would translate the indicator into explicit business logic, required fields, thresholds, expected outputs, and acceptance criteria. I would clarify ownership boundaries: Internal Audit defines the risk rationale and validates effectiveness, while Product and Data Engineering own production implementation and platform standards. After deployment, I would monitor precision, coverage, latency, and false-positive rates, then use investigation outcomes to refine the control. What approach would you take when operational data quality prevents a reliable conclusion? I would assess and quantify the issue, including completeness, timeliness, consistency, and likely impact on the analysis. I would avoid overstating results, identify what can and cannot be concluded, and propose immediate mitigations such as narrower filters or manual validation. For the longer term, I would partner with data and operational owners to improve upstream collection, establish quality checks, and track whether the remediation resolves the risk. GiveDirectly has delivered more than $1B in cash directly to 2+ million people living in poverty across 15 countries since 2011. We believe cash transfers are one of the most scalable, cost-effective, and dignified forms of aid, with the [research](https://www.givedirectly.org/gdresearch/) to back it up. Our work has been covered by [The Economist](https://www.economist.com/special-report/2024/01/10/givedirectly-does-what-it-says-on-the-tin), [NPR](https://www.npr.org/sections/goatsandsoda/2023/12/07/1217478771/its-one-of-the-biggest-experiments-in-fighting-global-poverty-now-the-results-ar), [TED](https://www.ted.com/talks/rory_stewart_to_end_extreme_poverty_give_cash_not_advice), and [The Washington Post](https://www.givedirectly.org/washingtonpost-apr-2024/). We are one of [Time100’s](https://time.com/collection/time100-most-influential-companies/2026/givedirectly/) Most Influential Companies of 2026. Our culture is candid, analytical, and non-hierarchical. We support high ownership and real professional growth. Curious about what it’s really like to work here? Read our [values](https://www.givedirectly.org/givedirectly-values/) and [hear from the people who do](https://www.youtube.com/watch?v=HbdS9vB_XYg). If they resonate, this could be a great fit! ### About this role 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. Reports to: Senior Manager of Internal Audit Level: Analyst / Manager 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](https://perfectwiki.page/p/iOeyfUha_9) 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. ### 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. ### Compensation 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](https://drive.google.com/file/d/1onxCdQX_DacG0812Wr99LF6zlMTg7RTP/view?usp=sharing). 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. ### Why work at GiveDirectly? 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 Working at GiveDirectly GiveDirectly is an Equal Opportunity Employer. All qualified applicants are considered for employment without regard to the person’s race, color, religion, national origin, sex, sexual orientation, age, marital status, veteran status, disability, or any other characteristic protected by applicable law. Flagging for US applicants: We invite you to “[Know Your Rights](https://app.getguru.com/card/TrxrEknc/US-Federal-Law-on-Workplace-Discrimination)” as an applicant. Commitment to Safeguarding As a global organization working with communities to eliminate extreme poverty, GiveDirectly takes the safeguarding of its recipients, staff, and partners seriously. To that end, GiveDirectly is a member of the [Misconduct Disclosure Scheme](https://misconduct-disclosure-scheme.org/), and will systematically check with previous employers about any abuse or misconduct related matters involving potential new hires. We may also employ other robust pre-hire screens, including in-depth reference checks, criminal background checks, and sanctions screens. These efforts help us continue to build and maintain trust with the communities we work with, and prevent abuse to our recipients and staff. **GD is committed to observing all local, national and international laws that protect people and basic human rights of all. GD is committed to a policy of “zero tolerance when it comes to preventing, reporting, or responding to any form of abuse or exploitation.” and expects anyone who works for GD to uphold the protection and safeguarding of our recipients as a priority.** Reasonable Accommodations We are committed to fostering an inclusive and accessible work environment. If you require any accommodations during the application or interview process, or to perform the essential functions of the role, please email us at careers@givedirectly.org with the email subject “Accommodation Needed”. We will work with you to ensure reasonable accommodations are made to support your needs. Want to put your best foot forward on your GiveDirectly application? Take a look at our [Candidate Application Prep Guide](https://drive.google.com/file/d/1JRyU81RnTGHQrw9nHTPw7oe_urTDh9JT/view?usp=sharing)! ### Referrals Know the right person for this role? Please consider [referring them](https://forms.gle/VpVpmQW7Sw9ryWRbA). We offer a reward of $3,000 for a Senior Manager or above role, and $500 for a Manager role, paid if we end up hiring your referral. Thank you for sending great people our way! 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