Tech Mahindra
Multinational IT services and consulting company.
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
Explain ROC AUC. Can a model have a high accuracy but a low AUC? Give an example.
#ROC AUC
#Evaluation Metrics
#Imbalanced Data
Data Scientist
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Technical
•
medium
Explain the difference between Random Forest and Gradient Boosting. When would you choose one over the other?
#Ensemble Learning
#Bagging
#Boosting
#Bias-Variance Tradeoff
Data Scientist
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Technical
•
medium
What is the Curse of Dimensionality, and what techniques do you use to mitigate it?
#Dimensionality Reduction
#PCA
#Feature Selection
Data Scientist
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Technical
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easy
How does L1 (Lasso) regularization differ from L2 (Ridge) regularization?
#Regularization
#Lasso
#Ridge
#Overfitting
Data Scientist
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Technical
•
medium
What evaluation metrics would you use for a highly skewed fraud detection dataset, and why?
#Evaluation Metrics
#Fraud Detection
#Precision-Recall
Data Scientist
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Technical
•
medium
Explain the working of the K-Means clustering algorithm. How do you choose the optimal 'K'?
#Clustering
#K-Means
#Elbow Method
#Silhouette Score
Data Scientist
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Technical
•
medium
What is Data Leakage in machine learning, and how can you prevent it during cross-validation?
#Data Leakage
#Cross-Validation
#Pipelines
Data Scientist
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Technical
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hard
Explain the mathematical intuition behind Support Vector Machines (SVM) and the kernel trick.
#SVM
#Math
#Kernel Trick
Data Scientist
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Technical
•
medium
What is SMOTE, and how does it work under the hood?
#SMOTE
#Imbalanced Data
#Algorithms
Data Scientist
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Technical
•
medium
In a telecom churn prediction project, the dataset is highly imbalanced (95% non-churn, 5% churn). How do you handle this?
#Imbalanced Data
#SMOTE
#Class Weights
#Evaluation Metrics
Machine Learning Engineer
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Technical
•
medium
What is the Curse of Dimensionality, and how does Principal Component Analysis (PCA) help mitigate it?
#Dimensionality Reduction
#PCA
Machine Learning Engineer
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Technical
•
medium
Explain L1 and L2 regularization. When would you prefer Lasso over Ridge?
#Regularization
#Linear Models
Machine Learning Engineer
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Technical
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hard
How does XGBoost handle missing values internally?
#XGBoost
#Tree Models
Machine Learning Engineer
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Technical
•
easy
What are the differences between bagging and boosting?
#Ensemble Methods
Machine Learning Engineer
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Technical
•
medium
Explain the difference between generative and discriminative models.
#Theory
#Modeling
Machine Learning Engineer
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Technical
•
easy
Explain the bias-variance tradeoff. How do you know if your model is overfitting?
#Theory
#Model Evaluation
Machine Learning Engineer
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Technical
•
medium
Explain the concept of cross-validation. Why is time-series cross-validation different from standard K-Fold?
#Validation
#Time Series
Machine Learning Engineer
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Technical
•
medium
Explain the difference between Random Forest and Gradient Boosting. Which would you use for a highly imbalanced dataset and why?
#Ensemble Methods
#Classification
Difficulty Radar
Based on recent AI-sourced data.
Meet Your Interviewers
The "Standard" Interviewer
Senior EngineerFocuses on core competencies, system constraints, and clear communication.
SimulateUnwritten Rules
Think Out Loud
Always explain your thought process before writing code or drawing architecture.