IBM
Global technology and consulting firm with deep roots in enterprise IT and AI.
3 Rounds
~14 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
•
medium
Explain the difference between L1 and L2 regularization. When would you use one over the other in a predictive model?
#Regularization
#Linear Models
#Feature Selection
Data Scientist
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Technical
•
medium
How does a Random Forest model handle missing values, and how does it compare to XGBoost in this regard?
#Tree Models
#Ensemble Methods
#Missing Data
Data Scientist
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Technical
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hard
Walk me through the mathematical intuition behind Support Vector Machines (SVM). What is the kernel trick?
#SVM
#Math
#Algorithms
Data Scientist
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Technical
•
medium
How do you handle highly imbalanced datasets in a fraud detection model?
#Imbalanced Data
#Classification
#Fraud Detection
Data Scientist
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Technical
•
medium
What evaluation metrics would you use for a multi-class classification problem where classes are imbalanced?
#Metrics
#Classification
Data Scientist
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Technical
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easy
Explain the Bias-Variance tradeoff. How do you identify if your model is suffering from high bias or high variance?
#Model Evaluation
#Theory
Data Scientist
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Technical
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easy
Describe the difference between bagging and boosting.
#Ensemble Methods
#Tree Models
Data Scientist
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Technical
•
medium
What is data leakage in machine learning, and how can you prevent it during feature engineering?
#Data Leakage
#Feature Engineering
#Best Practices
Difficulty Radar
Based on recent AI-sourced data.
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The "Standard" Interviewer
Senior EngineerFocuses on core competencies, system constraints, and clear communication.
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Think Out Loud
Always explain your thought process before writing code or drawing architecture.