Jana Small Finance Bank Data Scientist Interview Questions and Answers

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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Q1. Logistic regression loss function
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Q2. Step function in ML context
Ans. 
Step function is a function that returns a constant value for a certain range of inputs.
• In machine learning, step functions are used as activation functions in neural networks.

• They are typically used in binary classification problems where the output is either 0 or 1.

• Examples include Heaviside step function and sigmoid step function.
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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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Apptitude + two easy level coding questions , behavioural questions,

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. Focus was on SQL, projects done so far, Machine Learning and Stats
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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

Q1. Explain previous projects
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Q2. Explain 2 algo of your choice
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