Ernst & Young U.S. LLP Data Scientist Interview Questions
The questions to prepare for a Ernst & Young U.S. LLP Data Scientist interview. Questions from real interview reports rank first. Updated weekly.
Explain how supervised and unsupervised learning differ, and ground the distinction in a practical ML example.
Tests proficiency using window functions for analytics and feature engineering in SQL.
Find missing employee numbers in a fixed sequence using PostgreSQL generate_series and a left join.
Use ROW_NUMBER to return each student's second score on a specified Khan Academy exercise.
Khan AcademyFind the top 3 users by completed transaction volume in the last 30 days using joins and aggregation.
Revolut
ChimeExplain common machine learning evaluation metrics and when each is useful.
Design a production ML decision service with low latency serving, secure data handling, and scalable training and inference.
Explain what statistical significance means and why it matters when interpreting experimental or analytical results.
Outline the first checks to diagnose a sudden drop in a core product metric, starting with data quality, scope, and decomposition.
Identify the main pitfalls that can distort A/B test interpretation and explain how to guard against them.
Evaluates NLP modeling choices and end-to-end RAG system design.
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