Rakuten Data Scientist Interview Questions and Answers

Q1. Simple groupby questions in pandas?
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Q2. Group by questions in SQL?
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Q1. Transformer models.
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Related to Mathematical intuition in Deep learning.

Q1. General ML questions
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Q2. General dl. Questions
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Q1. Questions around the previous work
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Q2. Questions around how re-rank work, and image segmentation,
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Q3. Questions with podman and docker
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Q1. Project that I worked on
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Developed a predictive model for customer churn in a telecom company
• Used machine learning algorithms like logistic regression and random forest

• Analyzed customer data such as call duration, plan details, and customer complaints

• Achieved 85% accuracy in predicting customer churn
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Q2. Use case only verbal communication
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Q1. Explain bagging vs boosting
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Bagging and boosting are ensemble learning techniques used to improve the performance of machine learning models.
• Bagging (Bootstrap Aggregating) involves training multiple models independently on different subsets of the training data and then combining their predictions through averaging or voting.

• Boosting involves training multiple models sequentially, where each subsequent model corrects the errors made by the previ...read more
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How to judge if the comment is actually describing a product

Business Related case study, signed NDA

Q1. Count number of Parameters in BERT
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Q2. 3 sum array problem
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