Tech Mahindra

Multinational IT services and consulting company.

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 Cloud Engineer 35 Data Engineer 35 Data Scientist 35 DevOps Engineer 35 Frontend Engineer 35 Full Stack Engineer 35 Machine Learning Engineer 35 Product Manager 35 Software Engineer 35

All Topics Machine Learning 10 SQL 4 System Design 3 Statistics 2 Deep Learning 2 NLP 2 MLOps 2 Algorithms 2

Data Scientist • Behavioral • medium

Tell me about a time you had to explain a complex machine learning model's predictions to a non-technical client stakeholder.

#Stakeholder Management #Explainable AI #SHAP #LIME

Practice

Data Scientist • Behavioral • medium

Describe a situation where your model's performance degraded in production. How did you troubleshoot and resolve it?

#Model Drift #Monitoring #Debugging

Practice

Data Scientist • Behavioral • medium

How do you prioritize tasks when working on multiple client deliverables with tight, overlapping deadlines?

#Time Management #Prioritization #Client Management

Practice

Data Scientist • Behavioral • hard

Tell me about a time you disagreed with a client's approach to a data problem. How did you handle it?

#Conflict Resolution #Consulting #Client Interaction

Practice

Data Scientist • Behavioral • medium

Describe a project where you had to clean and process a massive, messy dataset. What was your approach?

#Data Cleaning #Big Data #Problem Solving

Practice

Data Scientist • Coding • medium

Write a SQL query to find the second highest salary of employees within each department.

#Window Functions #DENSE_RANK #Subqueries

Practice

Data Scientist • Coding • medium

Write a Pandas script to merge two large datasets on a common key, and fill missing numerical values with the mean of their respective categories.

#Pandas #Data Imputation #Groupby #Merge

Practice

Data Scientist • Coding • medium

Write a SQL query to calculate the 7-day rolling average of daily active users (DAU).

#Window Functions #Rolling Average #Date Functions

Practice

Data Scientist • Coding • hard

Implement a Python function to calculate the TF-IDF of a given corpus of text from scratch (without using Scikit-Learn).

#Python #TF-IDF #Math #NLP

Practice

Data Scientist • Coding • medium

Write a SQL query to find the top 3 customers by revenue in each region.

#Window Functions #RANK #CTEs

Practice

Data Scientist • Coding • easy

Write a Python program to detect and remove outliers from a Pandas DataFrame using the Interquartile Range (IQR) method.

#Python #Pandas #Outlier Detection #Statistics

Practice

Data Scientist • Coding • easy

Write a SQL query to find all employees who joined in the last 30 days and have the job title 'Data Scientist'.

#Date Functions #Filtering #WHERE Clause

Practice

Data Scientist • Coding • easy

Write a Python function to reverse a string without using built-in functions like reversed() or slicing [::-1].

#Python #Strings #Loops

Practice

Data Scientist • System Design • hard

Design a predictive maintenance system for a manufacturing client using IoT sensor data.

#IoT #Time Series #Streaming Data #Architecture

Practice

Data Scientist • System Design • medium

How would you deploy a machine learning model as a REST API using Docker and a cloud provider like AWS or Azure?

#Docker #FastAPI/Flask #Cloud Deployment #Containerization

Practice

Data Scientist • System Design • hard

Design a recommendation engine for a telecom provider's value-added services (e.g., Netflix bundles, international roaming).

#Recommendation Systems #Collaborative Filtering #Cold Start

Practice

Data Scientist • System Design • hard

Architect an MLOps pipeline for continuous training and deployment of a customer churn model.

#CI/CD #Model Registry #Airflow #Kubernetes

Practice

Data Scientist • System Design • hard

Design a real-time fraud detection system for credit card transactions.

#Real-time Processing #Fraud Detection #Latency #Architecture

Practice

Data Scientist • 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

Practice

Data Scientist • 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

Practice

Data Scientist • Technical • hard

How would you extract key entities and intent from unstructured IT support tickets to automate ticket routing?

#NER #Intent Classification #Transformers #Spacy

Practice

Data Scientist • Technical • medium

What are the core assumptions of Linear Regression, and how do you validate them?

#Linear Regression #Assumptions #Residual Analysis

Practice

Data Scientist • Technical • medium

What is the Curse of Dimensionality, and what techniques do you use to mitigate it?

#Dimensionality Reduction #PCA #Feature Selection

Practice

Data Scientist • Technical • hard

Explain the difference between ARIMA and LSTM for time series forecasting. When is one preferred over the other?

#ARIMA #LSTM #Deep Learning #Forecasting

Practice

Data Scientist • Technical • easy

How does L1 (Lasso) regularization differ from L2 (Ridge) regularization?

#Regularization #Lasso #Ridge #Overfitting

Practice

Data Scientist • Technical • medium

What evaluation metrics would you use for a highly skewed fraud detection dataset, and why?

#Evaluation Metrics #Fraud Detection #Precision-Recall

Practice

Data Scientist • Technical • hard

Explain the architecture of Transformers and the concept of the self-attention mechanism.

#Transformers #Self-Attention #NLP

Practice

Data Scientist • Technical • easy

What is a p-value? How do you use it to make decisions in A/B testing?

#Hypothesis Testing #A/B Testing #p-value

Practice

Data Scientist • 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

Practice

Data Scientist • Technical • medium

What is Data Leakage in machine learning, and how can you prevent it during cross-validation?

#Data Leakage #Cross-Validation #Pipelines

Practice

Data Scientist • Technical • medium

How do you prevent overfitting in deep neural networks?

#Overfitting #Dropout #Early Stopping #Regularization

Practice

Data Scientist • Technical • hard

Explain the mathematical intuition behind Support Vector Machines (SVM) and the kernel trick.

#SVM #Math #Kernel Trick

Practice

Data Scientist • Technical • medium

How do you handle out-of-vocabulary (OOV) words in NLP tasks?

#OOV #Tokenization #Word Embeddings #Subword Tokenization

Practice

Data Scientist • Technical • medium

What is SMOTE, and how does it work under the hood?

#SMOTE #Imbalanced Data #Algorithms

Practice

Data Scientist • Technical • medium

Explain ROC AUC. Can a model have a high accuracy but a low AUC? Give an example.

#ROC AUC #Evaluation Metrics #Imbalanced Data

Practice

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The "Standard" Interviewer

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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