Design fraud detection across channels with unknowns
Company: Amazon
Role: Data Scientist
Category: Machine Learning
Difficulty: hard
Interview Round: Onsite
Overview: This question evaluates a data scientist's competence in designing and operationalizing multi-channel fraud detection systems, covering cost-sensitive objective formulation, segmentation and feature engineering, model selection (including sequence and graph approaches), anomaly detection for unknown actors, drift monitoring, and safe LLM-assisted workflows. Commonly asked in the Machine Learning domain to assess end-to-end systems thinking and trade-off reasoning between customer experience and fraud loss, it tests both conceptual understanding and practical application-level skills such as evaluation, deployment guardrails, and handling label sparsity and delay.
Read the full Amazon Data Scientist interview experience this question came from
