How the interviewer opens it
“Design a machine learning system that estimates a borrower's probability of default for a lending decision. The lender needs reliable scores and clear reasons for each score.”
The prompt is vague on purpose. The first minutes are for you. Reduce the prompt to the core features and agree on the numbers before you draw.
What to clarify first
- Prediction target
- Scale
- Read and write mix
- Outcome delay and coverage
- Inputs and freshness
- Latency and availability
- Consistency and durability
- Fairness and legal review
- Model updates
- Decision ownership and exclusions
The interviewer has the answers. In a SysRound round, Alex gives these answers, as a real interviewer does.
Scope of a strong answer
Core requirements
- Score a loan application for a stated loan offer
- Train and test models with delayed loan outcomes
- Check probability calibration and fairness across borrower groups
- Return and retain score reasons for decision review
Usually out of scope
- Set loan prices or approval thresholds
- Build a credit bureau
- Collect overdue payments
- Generate final legal decision notices
What interviewers ask about
- Borrower outcomes
- Defaults appear months after the score. Monthly status snapshots can miss defaults before a cure. Only funded loans reveal the target outcome.
- Score meaning over time
- A useful borrower rank does not guarantee accurate probabilities. Long outcome delays hide current calibration errors and make economic changes hard to assess.
- Fairness across borrower groups
- Group metrics depend on the fairness goal and the loans with observed outcomes. Different group default rates can create conflicts between fairness goals.
- Reasons for a loan decision
- The lender must explain individual decisions. Correlated inputs and policy rules can make a model explanation incomplete or misleading.
Common mistakes
- Treat recent loans without reported defaults as negative examples
- Use monthly current status alone for a target that includes intervening defaults
- Train on information that arrived after the original score
- Claim accuracy for all applicants from funded loans alone
- Use ranking metrics without checking probability calibration
- Treat the absence of protected traits as proof of fairness
The bar by level
- Senior (L5/E5)
- Prevents temporal leakage and handles incomplete reports. Identifies selection bias. Separates calibration from ranking and model reasons from policy reasons. Designs reproducible scores and group audits.
- Staff (L6+/E6+)
- Defines what the available outcomes cannot establish. Sets report contracts and correction controls. Resolves fairness goals with legal and risk owners. Sets release gates and safe responses to economic change.