Calculate Profit of 4-Month Loan at 30% APR

Quick Overview

This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Calculate Profit of 4-Month Loan at 30% APR states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Calculate Profit of 4-Month Loan at 30% APR

Company: Affirm

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: easy

Interview Round: Technical Screen

##### Scenario Credit Risk – Short-term personal loan profitability evaluation ##### Question What is the profit of a 4-month loan with principal $1,000 and 30% APR when there is no compounding, inflation, or opportunity cost? ##### Hints Convert APR to monthly interest, multiply by four months, total interest equals profit.

Overview: This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Calculate Profit of 4-Month Loan at 30% APR states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

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Aug 4, 2025
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Calculate Profit of 4-Month Loan at 30% APR

Credit Risk — Short-term Personal Loan Profitability

Context

You are evaluating the profit on a simple-interest personal loan. Assume no compounding, inflation, opportunity cost, fees, defaults, or servicing expenses. The principal remains outstanding for the full term and is repaid at maturity.

Question

What is the profit on a 4-month loan with principal $1,000 and a 30% APR under these assumptions?

Clarifying Questions to Ask Guidance

  • Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.
  • State assumptions about instrumentation, randomization, sample size, and data quality.
  • Separate descriptive analysis from causal claims.

What a Strong Answer Covers Guidance

  • A metric framework with primary, guardrail, and diagnostic metrics.
  • A credible analysis or experiment design with clear assumptions and bias checks.
  • SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.
  • An actionable recommendation that explains trade-offs and next steps.

Follow-up Questions Guidance

  • What sanity checks would you run before trusting the result?
  • How would you handle novelty effects, seasonality, or selection bias?
  • What decision would you make if metrics disagree?
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