Hexaware Technologies Data Scientist Interview Questions and Answers

They gave a span of 3 days to build an AI-powered webapp

Q1. How would you go about learning a new skill
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Q2. Experience in cloud technologies
Ans. 
I have experience working with cloud technologies such as AWS, Azure, and Google Cloud Platform.
• Experience in setting up and managing virtual machines, storage, and networking in cloud environments

• Knowledge of cloud services like EC2, S3, RDS, and Lambda

• Experience with cloud-based data processing and analytics tools like AWS Glue and Google BigQuery
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Q1. Tell me about yourself
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Q2. Project details and challenges faced in the project
Ans. 
Developed a predictive model for customer churn in a telecom company
• Collected and cleaned customer data from various sources

• Performed exploratory data analysis to identify key factors influencing churn

• Built and fine-tuned machine learning models to predict customer churn

• Challenges included imbalanced data, feature engineering, and model interpretability
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Q1. 1. Basics of Python 2. Basics of ML like supervised and unsupervised learning
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Basic questions on data science, machine learning

Q1. Go through your resume and mentioned projects, most questions will be asked about your machine learning or data science projects
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Bulb and switch puzzle

Rope burning and length question

Q1. Why you want to join optum
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Basic DP, Array Questions

Q1. Resume Walkthrough and Discussion, Medium level coding questions
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Q1. Discussion with Manager
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Q1. Normal HR round
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Easy array questions.

Q1. Explain projects from resume.
Ans. 
Developed a machine learning model to predict customer churn for a telecom company.
• Used Python and scikit-learn to preprocess data and build the model

• Performed feature engineering to improve model performance

• Evaluated model using metrics like accuracy, precision, and recall
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Q2. Questions on image processing and computer vision.
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Q1. Different ML Algorithms
Ans. 
There are various ML algorithms such as linear regression, decision trees, random forests, SVM, KNN, neural networks, etc.
• Linear regression is used for predicting continuous values

• Decision trees and random forests are used for classification and regression

• SVM is used for classification and regression

• KNN is used for classification and regression

• Neural networks are used for complex problems such as image recognition and
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Q2. Python Libraries
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Leet code practice solve medium difficulty questions

Q1. Linear regression logistic regression deep learning nlp
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