
Mastercard Data Analyst Case Interview: Payments Analytics, Fraud Risk, and Experimentation
A 60‑minute, business-first analytics case modeled on Mastercard’s real data problems and culture of Decency Quotient (DQ). You’ll tackle an ambiguous scenario using anonymized card-transaction data to improve authorization rates and ROI while controlling fraud and ensuring inclusion. Expect to: 1) Frame the problem with stakeholder context (issuer, acquirer, merchant, cardholder) and define payments KPIs such as auth/approval rate, fraud rate, chargeback rate, cross‑border mix, and 3DS step‑up impact; 2) Write/describe SQL to join transactions, authorizations, merchants, and chargebacks; compute cohort and MCC-level trends; use windows for period-over-period deltas and outlier detection; 3) Design an experiment (Test & Learn–style) to evaluate a new fraud rule or 3DS policy—state hypothesis, success metrics, guardrails, sample sizing assumptions, segmentation (country, channel, MCC), and bias checks; 4) Diagnose data quality/privacy constraints (tokenization, PII minimization, PCI scope), discuss fairness and accessibility impacts on underserved segments, and articulate trade‑offs aligned with Mastercard’s inclusive, secure network; 5) Synthesize a concise executive readout with quantified impact, risks, and a phased rollout plan suitable for both technical and non‑technical stakeholders. Interviewers assess payments domain fluency, analytical rigor, SQL reasoning, experiment design, data ethics aligned to DQ, and clarity under time pressure.
8 minutes
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About This Interview
Interview Type
PRODUCT SENSE
Difficulty Level
3/5
Interview Tips
• Research the company thoroughly
• Practice common questions
• Prepare your STAR method responses
• Dress appropriately for the role