capital-one

Capital One Data Analyst Case Interview — Credit Card Profitability, Experimentation, and SQL

This 60-minute, interviewer-led case mirrors Capital One’s test-and-learn, hypothesis-driven culture and focuses on analytics for the Credit Card business. You will work through an end-to-end, data-backed recommendation while demonstrating structured thinking, quantitative rigor, and clear communication. What the case covers (typical flow): - Business context: You’re the analyst for a credit card portfolio. Leadership is considering a change (e.g., increasing credit lines for a subsegment, launching a new acquisition offer, or modifying rewards). Your goal is to assess profitability and risk, validate with data, and recommend next steps. - Data packet: Small, curated tables (printed or shared) with account/offer/test data. Columns often include segment flags (FICO bands, tenure), offer/test flags, approvals, balances, APR, revolve rate, interchange rate, annual fee, rewards cost, charge-offs, 30/60/90+ DPD, acquisition cost, and operational costs. You may also get simple cohort curves or pre/post metrics. - Quant analysis: Do back-of-the-envelope math and table calcs to size financial impact and assess risk. Common metrics: • Profit per account (or per approved account) • Expected loss = PD × LGD × EAD • Revenue components: interest income, interchange, fees; minus rewards, funding, acquisition amortization, ops cost, expected loss • NPV or payback framing for multi-period effects - Experimentation/A/B testing: Evaluate a test vs. control offer or feature. Tasks can include computing lift, sanity checks (sample ratio mismatch), basic significance reasoning (two-proportion logic), practical vs. statistical significance, and guardrail metrics (approval rate, charge-off rate, CSAT proxy). - SQL/data reasoning: You may be asked to outline a query (verbally or on a whiteboard) to join offer/test tables to outcomes, aggregate by segment, and compute rates. Typical prompts: calculate 90+ DPD by cohort; compute approval and charge-off rates by FICO band; identify top drivers of profit variance; filter to first 90 days post-activation. - Risk and customer lens: Discuss trade-offs (approval vs. loss rates, rewards cost vs. spend lift), potential adverse selection, operational risk, and customer experience. Propose a targeted rollout or additional test if uncertainty remains. - Synthesis and recommendation: Present a crisp, MECE-structured answer with a point of view, supporting numbers, assumptions, risks, and next steps (e.g., expand to specific subsegments, add guardrails, or halt and redesign the offer). Interview style and expectations (Capital One-specific): - Structured problem solving with frequent “why?” and “so what?” follow-ups; interviewers expect clear, top-down communication and defensible assumptions. - Comfort with mental math/Excel-style calculations; precision matters, but sensible approximations are fine if you show your work. - Test-and-learn mindset: propose measurable success metrics, guardrails (loss/DPD thresholds), and an experiment plan. - Practical SQL fluency: knowing how you’d build the dataset and compute metrics is often more important than perfect syntax. Time guide (typical): 5 min case brief; 30 min analysis (quant + SQL reasoning + experiment readout); 10 min deep dive/Q&A; 10 min recommendation and risks; 5 min wrap. What strong performance looks like: - Clear problem framing and hypotheses; translates the business question into an analytic plan - Correct and transparent calculations; ties numbers to profit and risk impact - Sound experiment interpretation; identifies caveats and proposes next steps - Business judgment with customer empathy; recommends a prudent, testable path forward - Concise, persuasive communication tailored to a product/credit-risk audience Common pitfalls: jumping to an answer without sizing impact; ignoring expected losses/charge-offs; over-indexing on p-values without business significance; unfocused SQL logic; weak story in the final recommendation.

engineering

8 minutes

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About This Interview

Interview Type

PRODUCT SENSE

Difficulty Level

4/5

Interview Tips

• Research the company thoroughly

• Practice common questions

• Prepare your STAR method responses

• Dress appropriately for the role