paypal

PayPal Data Analyst Case Interview: Checkout Authorization, Fraud Trade‑offs, and Funnel Optimization

Overview: A 60–75 minute case modeled on PayPal’s product analytics and risk-centric interview style. You will analyze a realistic checkout scenario spanning PayPal/Braintree card and wallet flows, quantify trade‑offs between authorization rate, conversion, and loss, and recommend an experiment or policy change. Expect to demonstrate crisp SQL thinking, practical experimentation skills, and stakeholder-ready storytelling aligned to PayPal’s focus on trust, compliance, and scaled payments. Business context: A cohort of US SMB merchants using PayPal/Braintree reports a drop in successful authorizations for cross‑border shoppers after rollout of new risk rules and increased 3‑D Secure prompts. PMs want to raise authorized TPV while staying within risk guardrails and maintaining a great buyer experience. Data you receive (synthetic, column names representative of PayPal-style event/data warehouse schemas): - payments_fact(txn_id, merchant_id, user_id_hash, payment_method, product_surface, country_buyer, country_merchant, currency, amount, checkout_started_ts, auth_submitted_ts, auth_status, capture_status) - risk_decisions(txn_id, risk_rule_version, risk_outcome, risk_score, 3ds_triggered, 3ds_outcome) - chargebacks(txn_id, cb_create_ts, cb_reason_code) - ab_exposure(user_id_hash, experiment_key, variant, exposure_ts) - merchants_dim(merchant_id, segment, vertical, processing_platform) What you’re asked to do: 1) Clarify goals and constraints: Define primary and guardrail metrics for this problem. Typical KPIs include auth_rate, end‑to‑end conversion, fraud/chargeback proxy, and authorized_TPV. State assumptions (e.g., acceptable loss bands, seasonality, logging latency, PCI/PII handling) and align to PayPal’s customer trust posture. 2) Build the funnel: From checkout_started → auth_submitted → authorized → captured. Quantify leaks by merchant segment and cross‑border vs domestic. Identify whether the drop concentrates in specific markets, instruments, or 3‑D Secure outcomes. 3) Segment and diagnose: Compare cohorts by 3ds_triggered and 3ds_outcome; evaluate impact of risk_rule_version. Control for currency and merchant vertical to isolate effects. Call out data quality checks (sample ratio mismatches, timestamp gaps, duplicate txn_id). 4) Experiment readout: You discover an existing experiment (experiment_key='risk_3ds_v2') with Control vs Treatment (stricter 3DS triggering). Compute uplift/delta on auth_rate, conversion, authorized_TPV, and a loss proxy (chargebacks per authorized txn). Perform a quick statistical check (CIs or simple z‑test) and sanity checks (balanced exposure, invariant metrics). 5) Recommendation: Propose an actionable change (e.g., region‑ and device‑aware 3DS triggering, merchant‑segment allowlist, or retry logic on soft declines). Quantify impact with a back‑of‑envelope: incremental authorized_TPV × take_rate minus expected incremental loss. Include rollout plan, monitoring, and a privacy/compliance note. 6) Communication: Conclude with a concise, executive‑level narrative tailored to a PM and a Risk partner, plus a one‑slide/tableau‑style summary the interviewer can screenshot. What good looks like (rubric hints): - Analytical rigor: Correct metric definitions, thoughtful guardrails, acknowledgment of cross‑border nuances and FX/currency confounders. - SQL/logic: Clean joins on txn_id and user_id_hash, deduping, windowing for funnels, careful handling of late captures and partial reversals. - Experimentation: Sound inference with practical guardrails (power, SRM checks), not over‑claiming significance. - Product sense: Solutions that balance buyer friction, merchant conversion, and platform risk—reflecting PayPal’s trust‑first culture at global scale. - Storytelling: Clear trade‑off framing, measurable next steps, and monitoring plan (e.g., weekly dashboards on auth_rate, false declines, dispute trend). Interviewer prompts (examples): - “Auth success fell 2.1pp week‑over‑week for cross‑border EU buyers; where do you look first?” - “Control auth_rate=86.4%, Treatment=88.1%, but chargeback proxy rose 7bps. Ship, hold, or iterate? Why?” - “If we relax 3DS on trusted devices for low‑risk carts, estimate the revenue and loss impact for US→UK flows.” Constraints and norms specific to PayPal: Expect attention to compliance/privacy (no raw PAN/PII; use tokenized joins), global market differences, and alignment with merchant experience. Interviewers value pragmatic solutions that scale across brands (PayPal, Braintree, Venmo) and markets while upholding customer trust.

engineering

8 minutes

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About This Interview

Interview Type

PRODUCT SENSE

Difficulty Level

4/5

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