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Bajaj Finance Data Scientist Interview Questions and Answers
Bajaj Finance Data Scientist Interview Experiences
2 interviews found
I was a test in our college of about 45min revolving around aptitude.
Few basic coding questions.
(2 Questions)
- Q1. About linear and logistic regression
- Q2. About svm and kernels
Adidas Boota
(1 Question)
- Q1. Azure Data Lake, Prediction model
Top trending discussions
Interview questions from similar companies
(2 Questions)
- Q1. Common ways to evaluate Time Series model
- Ans.
Common ways to evaluate Time Series model include AIC, BIC, RMSE, MAE, ACF, PACF, etc.
Use Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to compare models
Calculate Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) to assess model accuracy
Analyze Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) to check for autocorrelation in residuals
- Q2. Best ways to handle multicollinearity
- Ans.
Use techniques like feature selection, regularization, PCA, and VIF to handle multicollinearity.
Perform feature selection to choose the most relevant variables for the model.
Apply regularization techniques like Lasso or Ridge regression to penalize high coefficients.
Utilize Principal Component Analysis (PCA) to reduce dimensionality and decorrelate variables.
Check for Variance Inflation Factor (VIF) to identify highly
(2 Questions)
- Q1. Write a function taking input as string and output a dictionary which will give key as characters in these string and values as their frequency of occurrence
- Q2. TF IDF in NLP
- Ans.
TF IDF is a technique used in NLP to measure the importance of a word in a document within a collection of documents.
TF IDF stands for Term Frequency-Inverse Document Frequency.
It is used to determine how important a word is in a document relative to a collection of documents.
TF IDF is calculated by multiplying the term frequency (TF) of a word in a document by the inverse document frequency (IDF) of the word across al...
Skills evaluated in this interview
Create data frame, make histogram
Interview Preparation Tips
Selected
I applied via Campus Placement and was interviewed before Jul 2023. There were 2 interview rounds.
Questions on Prob Stats, ML
(2 Questions)
- Q1. Models you have worked on
- Q2. Internship Experience
Data Scientist Interview Questions & Answers
Bharat Financial Inclusion
Anonymous
posted on 6 Sep 2021
I applied via Recruitment Consultant and was interviewed in Mar 2021. There were 4 interview rounds.
Interview Questionnaire
4 Questions
- Q1. How familar with python,SQL,and ml models
- Ans.
I am very familiar with Python, SQL, and ML models.
I have extensive experience using Python for data analysis and machine learning tasks.
I am proficient in writing SQL queries to extract data from databases.
I have worked with a variety of ML models, including regression, classification, and clustering.
I am familiar with popular ML libraries such as scikit-learn, TensorFlow, and Keras.
I have experience with data preproc...
- Q2. Ml Classification models including type of metrics
- Ans.
ML classification models use various metrics to evaluate performance.
Common metrics include accuracy, precision, recall, F1 score, and AUC-ROC.
Accuracy measures the proportion of correct predictions.
Precision measures the proportion of true positives among all positive predictions.
Recall measures the proportion of true positives among all actual positives.
F1 score is the harmonic mean of precision and recall.
AUC-ROC me...
- Q3. How you handle data when outliers are in data
- Ans.
Outliers can significantly impact analysis. It's important to identify and handle them appropriately.
Visualize the data to identify outliers
Consider the source of the outliers and whether they are valid data points
Remove outliers if they are invalid or use robust statistical methods that are less sensitive to outliers
Document any handling of outliers in the analysis report
- Q4. What Type of statistics used In earlier organization to analyse and build models
- Ans.
The organization used descriptive and inferential statistics to analyze and build models.
Descriptive statistics were used to summarize and describe the data, such as mean, median, and standard deviation.
Inferential statistics were used to make predictions and draw conclusions about the population based on the sample data, such as hypothesis testing and regression analysis.
The organization may have also used time series...
Interview Preparation Tips
Skills evaluated in this interview
Selected
I applied via Referral and was interviewed in Jul 2024. There were 3 interview rounds.
(1 Question)
- Q1. Basic statistics and ML questions.
(1 Question)
- Q1. Face to face interview round. Asked me to write codes on paper
(1 Question)
- Q1. Good HR, compensation was better
(2 Questions)
- Q1. Common ways to evaluate Time Series model
- Ans.
Common ways to evaluate Time Series model include AIC, BIC, RMSE, MAE, ACF, PACF, etc.
Use Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to compare models
Calculate Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) to assess model accuracy
Analyze Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) to check for autocorrelation in residuals
- Q2. Best ways to handle multicollinearity
- Ans.
Use techniques like feature selection, regularization, PCA, and VIF to handle multicollinearity.
Perform feature selection to choose the most relevant variables for the model.
Apply regularization techniques like Lasso or Ridge regression to penalize high coefficients.
Utilize Principal Component Analysis (PCA) to reduce dimensionality and decorrelate variables.
Check for Variance Inflation Factor (VIF) to identify highly
(2 Questions)
- Q1. Write a function taking input as string and output a dictionary which will give key as characters in these string and values as their frequency of occurrence
- Q2. TF IDF in NLP
- Ans.
TF IDF is a technique used in NLP to measure the importance of a word in a document within a collection of documents.
TF IDF stands for Term Frequency-Inverse Document Frequency.
It is used to determine how important a word is in a document relative to a collection of documents.
TF IDF is calculated by multiplying the term frequency (TF) of a word in a document by the inverse document frequency (IDF) of the word across al...
Skills evaluated in this interview
Create data frame, make histogram
Interview Preparation Tips
Selected
I applied via Campus Placement and was interviewed before Jul 2023. There were 2 interview rounds.
Questions on Prob Stats, ML
(2 Questions)
- Q1. Models you have worked on
- Q2. Internship Experience
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Bajaj Finance Data Scientist Interview Process
based on 2 interviews
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