Capgemini

Global leader in partnering with companies to transform and manage their business by harnessing the power of technology.

4 Rounds ~21 Days Medium

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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

All Roles Data Scientist 8

All Topics Machine Learning 8 SQL 4 System Design 4 Algorithms 3 Python 2 Communication 2 Generative AI 2 MLOps 2

Data Scientist • Technical • easy

Explain the difference between Bagging and Boosting. Give an example of an algorithm for each.

#Ensemble Methods #Random Forest #XGBoost

Practice

Data Scientist • Technical • medium

How do you handle highly imbalanced datasets in a fraud detection project? What metrics would you use to evaluate your model?

#Imbalanced Data #SMOTE #Evaluation Metrics

Practice

Data Scientist • Technical • medium

How do you evaluate a clustering model when ground truth labels are not available?

#Unsupervised Learning #Clustering #Evaluation Metrics

Practice

Data Scientist • Technical • easy

What are the key differences between L1 (Lasso) and L2 (Ridge) regularization? When would you use one over the other?

#Regularization #Linear Models #Feature Selection

Practice

Data Scientist • Technical • medium

Why would you choose XGBoost over a Random Forest for a tabular dataset?

#XGBoost #Random Forest #Model Selection

Practice

Data Scientist • Technical • medium

Explain the ROC curve and AUC. When would you use Precision-Recall AUC instead of ROC-AUC?

#Evaluation Metrics #Classification

Practice

Data Scientist • Technical • medium

How does a Support Vector Machine (SVM) handle non-linear data?

#SVM #Kernel Trick #Math

Practice

Data Scientist • Technical • medium

What is target leakage in machine learning, and how do you prevent it during feature engineering?

#Data Leakage #Feature Engineering #Model Validation

Practice

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Senior Engineer

Focuses 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.

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