EY
Ernst & Young Global Limited, a multinational professional services partnership.
4 Rounds
~21 Days
Medium
The Interview Loop
Recruiter Screen (30 min)
Standard fit check, behavioral questions, and resume overview.
Technical Loop (3-4 Rounds)
Deep dive into domain knowledge, coding, and system design.
Interview Question Bank
Data Scientist
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Technical
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medium
How would you evaluate the performance of an Unsupervised Learning model, specifically a K-Means clustering algorithm used to segment retail customers?
#Unsupervised Learning
#Clustering
#Evaluation Metrics
Data Scientist
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Technical
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medium
In financial crime consulting, we often deal with highly imbalanced datasets (e.g., 0.01% fraud cases). How would you approach building and evaluating a machine learning model for this scenario?
#Imbalanced Data
#Fraud Detection
#Evaluation Metrics
#SMOTE
Data Scientist
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Technical
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medium
What is the bias-variance tradeoff? How does it apply when tuning hyperparameters for a Random Forest model predicting audit anomalies?
#Model Theory
#Bias-Variance Tradeoff
#Random Forest
#Hyperparameter Tuning
Data Scientist
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Technical
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medium
Explain the difference between L1 (Lasso) and L2 (Ridge) regularization. In what EY consulting scenario would you prefer L1 over L2?
#Regularization
#Linear Models
#Feature Selection
Data Scientist
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Technical
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hard
Explain the mathematical intuition behind Gradient Boosting. How does it differ from AdaBoost?
#Ensemble Methods
#Gradient Boosting
#Algorithm Theory
Machine Learning Engineer
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Technical
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medium
How do you handle highly imbalanced datasets, such as in a credit card fraud detection model where fraud represents 0.1% of the data?
#Classification
#Data Sampling
#Metrics
Machine Learning Engineer
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Technical
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easy
Explain the difference between Bagging and Boosting. Give an example of an algorithm for each.
#Ensemble Methods
#Random Forest
#XGBoost
Machine Learning Engineer
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Technical
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medium
What are the key differences between L1 (Lasso) and L2 (Ridge) regularization, and when would you use each?
#Regularization
#Linear Models
Machine Learning Engineer
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Technical
•
medium
How does XGBoost handle missing values internally?
#XGBoost
#Algorithms
Machine Learning Engineer
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Technical
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easy
Explain the trade-off between precision and recall. Which is more important in medical diagnosis vs spam detection?
#Metrics
#Evaluation
Difficulty Radar
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Senior EngineerFocuses on core competencies, system constraints, and clear communication.
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