LTIMindtree
Global technology consulting and digital solutions 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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Behavioral
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medium
Tell me about a time you had to explain a complex machine learning model's predictions to a non-technical business stakeholder.
#Stakeholder Management
#Explainable AI
#Communication
Data Scientist
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Behavioral
•
hard
Describe a situation where your model performed exceptionally well in training and testing but failed in production. How did you debug and fix it?
#Debugging
#Data Drift
#Production ML
Data Scientist
•
Behavioral
•
easy
Why do you want to join LTIMindtree, and how does your experience align with our focus on digital transformation for enterprise clients?
#Company Knowledge
#Motivation
#Consulting
Data Scientist
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Behavioral
•
medium
Tell me about a time you had to work with a difficult client or team member to deliver a data science project on time.
#Conflict Resolution
#Teamwork
#Client Management
Data Scientist
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Coding
•
easy
Write a Python function using Pandas to calculate the 7-day rolling average of daily sales data for a retail client.
#Python
#Pandas
#Time Series
#Data Manipulation
Data Scientist
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Coding
•
medium
Implement a custom cross-validation split function in Python for time-series data without using scikit-learn's TimeSeriesSplit.
#Python
#Time Series
#Cross Validation
#Algorithms
Data Scientist
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Coding
•
easy
Given a dictionary of employee names and their salaries, write a Python script to find the second highest salary efficiently.
#Python
#Data Structures
#Sorting
Data Scientist
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Coding
•
medium
Write a SQL query to find all customers who purchased a product in three consecutive months.
#SQL
#Window Functions
#Date Functions
Data Scientist
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Coding
•
medium
Calculate the cumulative sum of revenue partitioned by geographic region and ordered by transaction date.
#SQL
#Window Functions
#Aggregations
Data Scientist
•
Coding
•
hard
Identify duplicate records in a massive transaction table and write a query to delete them, keeping only the row with the lowest ID, without using a temporary table.
#SQL
#Data Cleaning
#CTEs
Data Scientist
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Coding
•
medium
Given a string, write a Python program to find the length of the longest substring without repeating characters.
#Python
#Sliding Window
#Strings
Data Scientist
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Coding
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medium
Write a Pandas script to merge two datasets on a common key, but only keep rows where a specific column's value is above the 75th percentile of the merged dataset.
#Python
#Pandas
#Data Merging
#Statistical Filtering
Data Scientist
•
Coding
•
medium
Write a SQL query to find the top 3 highest-grossing products per category. Assume a 'sales' table and a 'products' table.
#SQL
#Window Functions
#Joins
Data Scientist
•
Coding
•
medium
Implement a Python function from scratch to calculate the cosine similarity between two sparse vectors represented as dictionaries.
#Python
#Math
#Vectors
#Algorithms
Data Scientist
•
Coding
•
medium
Write a Python generator function to read a massive 50GB CSV file line by line, process it, and yield the result to prevent memory overflow.
#Python
#Generators
#Memory Management
#Big Data
Data Scientist
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System Design
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medium
Design a churn prediction system for a telecom client. What features would you engineer, and how would you frame the target variable?
#System Design
#Feature Engineering
#Classification
Data Scientist
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System Design
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medium
Walk me through how you would deploy a trained machine learning model as a REST API using FastAPI on Azure.
#Azure
#FastAPI
#Deployment
#Docker
Data Scientist
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System Design
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hard
A retail client wants to forecast inventory demand for 10,000 SKUs for the next 4 weeks. Walk me through your end-to-end approach.
#Forecasting
#System Design
#Scalability
#ARIMA/Prophet/LGBM
Data Scientist
•
System Design
•
medium
Explain how you would build a hybrid recommendation engine for an e-commerce platform.
#Collaborative Filtering
#Content-Based Filtering
#System Design
Data Scientist
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System Design
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hard
We have a client in the BFSI sector looking to automate loan approvals using ML. How do you ensure the model is fair, unbiased, and compliant with regulations?
#Fairness
#Bias
#BFSI
#Regulatory Compliance
Data Scientist
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System Design
•
medium
How do you monitor data drift and concept drift in a deployed machine learning model? What actions do you take if drift is detected?
#MLOps
#Model Monitoring
#Drift
Data Scientist
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Technical
•
medium
Explain the bias-variance tradeoff. How does this concept apply differently to Random Forests compared to Gradient Boosting Machines?
#Theory
#Ensemble Methods
#Model Evaluation
Data Scientist
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Technical
•
medium
We are building a fraud detection model for a banking client where the fraud rate is 0.01%. How do you handle this highly imbalanced dataset?
#Imbalanced Data
#SMOTE
#Class Weights
#Evaluation Metrics
Data Scientist
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Technical
•
hard
Explain the mathematical intuition behind Support Vector Machines (SVM) and the kernel trick. When would you use an RBF kernel over a linear kernel?
#SVM
#Mathematics
#Kernels
Data Scientist
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Technical
•
medium
What is the difference between L1 (Lasso) and L2 (Ridge) regularization? How do they affect feature selection?
#Regularization
#Feature Selection
#Linear Models
Data Scientist
•
Technical
•
medium
How does XGBoost handle missing values internally without requiring explicit imputation beforehand?
#XGBoost
#Missing Data
#Tree Algorithms
Data Scientist
•
Technical
•
hard
Explain the architecture of Transformers and the mathematical mechanism behind scaled dot-product self-attention.
#Deep Learning
#Transformers
#Attention Mechanism
Data Scientist
•
Technical
•
medium
How would you extract specific entities like invoice numbers, dates, and amounts from a large corpus of unstructured PDF documents?
#OCR
#NER
#Information Extraction
Data Scientist
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Technical
•
hard
How do you implement a Retrieval-Augmented Generation (RAG) pipeline for a corporate knowledge base? Discuss vector databases and chunking strategies.
#RAG
#LLMs
#Vector Databases
#Embeddings
Data Scientist
•
Technical
•
medium
Explain the difference between fine-tuning an LLM (like Llama 2) and using prompt engineering with few-shot learning. When would you choose which?
#LLMs
#Fine-tuning
#Prompt Engineering
Data Scientist
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Technical
•
medium
What are the core assumptions of Linear Regression? How do you detect and correct for heteroscedasticity?
#Linear Regression
#Statistics
#Assumptions
Data Scientist
•
Technical
•
easy
Explain the fundamental difference between bagging and boosting ensemble methods.
#Ensemble Methods
#Bagging
#Boosting
Data Scientist
•
Technical
•
medium
How do you choose the optimal number of clusters in a K-Means algorithm? Explain how the Silhouette score works.
#Clustering
#K-Means
#Evaluation Metrics
Data Scientist
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Technical
•
medium
What evaluation metrics would you use for a multi-class classification problem where the classes are highly imbalanced?
#Metrics
#Multi-class
#Imbalanced Data
Data Scientist
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Technical
•
easy
What is the difference between Word2Vec and TF-IDF? In what scenarios would you choose one over the other?
#NLP
#Embeddings
#Text Processing
Difficulty Radar
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Senior EngineerFocuses on core competencies, system constraints, and clear communication.
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