RBL Bank Data Scientist Interview Questions and Answers

Hard level aptitude questions... Time taking sums

Q1. Puzzles and probability questions
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Q2. Asking completely about data science projects in your resume
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Q1. Just asked some puzzle questions
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Q2. Asked if iam willing to relocate and about my family
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Aptitude test for about an hour.

Q1. Questions related to data science were asked based on your resume. Puzzles were asked too. Mostly they were from gfg.
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Q2. What are parameters used in a random forest.
Ans. 
Parameters used in a random forest include number of trees, maximum depth of trees, minimum samples split, and maximum features.
• Number of trees: The number of decision trees to be used in the random forest.

• Maximum depth of trees: The maximum depth allowed for each decision tree.

• Minimum samples split: The minimum number of samples required to split a node.

• Maximum features: The maximum number of features to consider when
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The first technical round will cover how computer vision works, including the advantages and disadvantages of regression and random forest. It will also include discussions on when to use precision and recall, methods to reduce false positives, and criteria for selecting different models. Additionally, disadvantages of PCA will be addressed, along with project-related questions. The second round will focus on standard aptitude tests, while the third round will involve a casual conversation with the Executive Vice President.

Normal aptitude questions

It was okay as the interview was scheduled on time.

Q1. They asked questions in the interview Including Python, Machine learning, SQL, Power BI
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If else conditions, data merging, datetime conversions, EDA on a sample data set, duplicates removal and missing value imputation

Q1. If you have to sell a kids account, how will you Target the customer. What features will you build?
Ans. 
To target customers for a kids account, focus on features like parental controls, educational content, and interactive games.
• Implement parental controls to assure parents of child safety online.

• Include educational content to attract parents looking for learning opportunities.

• Incorporate interactive games to engage children and make the account more appealing.

• Offer rewards or incentives for completing educational activi...read more
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Q1. Machine learning algorithms.
Ans. 
Machine learning algorithms are tools used to analyze data, identify patterns, and make predictions without being explicitly programmed.
• Machine learning algorithms can be categorized into supervised, unsupervised, and reinforcement learning.

• Examples of machine learning algorithms include linear regression, decision trees, support vector machines, and neural networks.

• These algorithms require training data to learn patte...read more
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Q2. Credit risk life cycle
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Q3. Pandas related questions
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Q1. Steps of developing a credit risk model
Ans. 
Developing a credit risk model involves several steps to assess the likelihood of a borrower defaulting on a loan.
• 1. Define the problem and objectives of the credit risk model.

• 2. Gather relevant data such as credit history, income, debt-to-income ratio, etc.

• 3. Preprocess the data by handling missing values, encoding categorical variables, and scaling features.

• 4. Select a suitable machine learning algorithm such as logi...read more
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Q2. Pandas related questions
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Q3. Bagging and Boosting
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Q1. Explain AIC and BIC
Ans. 
AIC and BIC are statistical measures used for model selection in the context of regression analysis.
• AIC (Akaike Information Criterion) is used to compare the goodness of fit of different models. It penalizes the model for the number of parameters used.

• BIC (Bayesian Information Criterion) is similar to AIC but penalizes more heavily for the number of parameters, making it more suitable for model selection when the focus...read more
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Q2. Difference between xgboost and lightgbm
Ans. 
XGBoost is a popular gradient boosting library while LightGBM is a faster and more memory-efficient alternative.
• XGBoost is known for its accuracy and performance on structured/tabular data.

• LightGBM is faster and more memory-efficient, making it suitable for large datasets.

• LightGBM uses a histogram-based algorithm for splitting whereas XGBoost uses a level-wise tree growth strategy.
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Q3. Bagging and boosting
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