Kantar Data Scientist Interview Questions and Answers

Q1. Explain the RAG pipeline?
Ans. 
RAG pipeline is a data processing pipeline used in data science to categorize data into Red, Amber, and Green based on certain criteria.
• RAG stands for Red, Amber, Green which are used to categorize data based on certain criteria

• Red category typically represents data that needs immediate attention or action

• Amber category represents data that requires monitoring or further investigation

• Green category represents data that...read more
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Q2. Explain Confusion metrics
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Confusion metrics are used to evaluate the performance of a classification model by comparing predicted values with actual values.
• Confusion matrix is a table that describes the performance of a classification model.

• It consists of four different metrics: True Positive, True Negative, False Positive, and False Negative.

• These metrics are used to calculate other evaluation metrics like accuracy, precision, recall, and F1 s...read more
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Basic aptitude , tech aptitude

Q1. What is overfitting
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Q2. How to handle missing values
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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. Past projects of machine learning
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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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I was asked to write SQL queries for 3rd highest salary of the employee, some name filtering, group by tasks.
Python code to find the index of the maximum number without using numpy.

Q1. Explain the Project undertaken during the research and follow-up questions
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Q1. Write pandas query to separate the names as first and last name from the full name. Drop the duplicate columns and also the missing values. Write output for the Python code. Write SQL query to retrieve t...read more
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Answering questions related to data science concepts and techniques.
• Recall is the ratio of correctly predicted positive observations to the total actual positives. Precision is the ratio of correctly predicted positive observations to the total predicted positives.

• To reduce variance in an ensemble model, techniques like bagging, boosting, and stacking can be used. Bagging involves training multiple models on different ...read more
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Q1. Questions related to python basics.
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Q2. Basic calculus to test math skills.
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Q3. Machine learning metrics.
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Q1. Policies, behavioral round.
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Q2. Work culture and the selection process.
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Q3. Discussion about previous employers and educational background.
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