Dabur Research Foundation Research Scientist 1 Interview Questions and Answers

1 hour aptitude test

Q1. What is one hot encoding
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Q1. What is your long term goal
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Aptitude test consists of 40 questions.

Q1. Introduce yourself with ur projects
Ans. 
I am a data scientist with experience in developing predictive models and analyzing large datasets.
• Developed a predictive model for customer churn prediction using machine learning algorithms

• Analyzed sales data to identify key trends and patterns for business optimization

• Implemented natural language processing techniques for sentiment analysis of customer reviews
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Q2. Problem statement of a case that needs solutions.
Ans. 
Develop a predictive model to identify potential customers for a new product launch.
• Define the target variable and features to be used in the model

• Collect and preprocess relevant data for training the model

• Select an appropriate machine learning algorithm and train the model

• Evaluate the model's performance using metrics like accuracy, precision, and recall

• Use the model to predict potential customers for the new product
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Q1. Cross validation
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Q2. How to handle imbalanced dataset
Ans. 
Handling imbalanced datasets involves techniques like resampling, using different algorithms, and adjusting class weights.
• Use resampling techniques like oversampling the minority class or undersampling the majority class.

• Utilize algorithms that are robust to imbalanced datasets, such as Random Forest, XGBoost, or SVM.

• Adjust class weights in the model to give more importance to the minority class.

• Use techniques like SMO...read more
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SQL, Python coding …

Given 6 coding qns related to java and html and also ML.

Q1. Explain abt projects and qns related to ML.
Ans. 
Projects in machine learning involve developing algorithms to analyze and interpret data for various applications.
• Developing a recommendation system for an e-commerce website

• Predicting customer churn for a telecommunications company

• Classifying images in a computer vision project

• Anomaly detection in network traffic for cybersecurity

• Natural language processing for sentiment analysis
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Q1. Basic hr qns why straive?
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Q1. Past projects of machine learning
Ans. 
Developed a predictive model for customer churn using machine learning algorithms.
• Used Python and scikit-learn library for data preprocessing and model building

• Performed feature engineering to improve model performance

• Evaluated model performance using metrics like accuracy, precision, and recall
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Q2. Distributed computing and spark questions
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