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TVS Credit Data Scientist Interview Questions and Answers

Updated 2 Jul 2024

TVS Credit Data Scientist Interview Experiences

1 interview found

Interview experience
4
Good
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Campus Placement and was interviewed before Jul 2023. There were 2 interview rounds.

Round 1 - Aptitude Test 

Questions on Prob Stats, ML

Round 2 - One-on-one 

(2 Questions)

  • Q1. Models you have worked on
  • Q2. Internship Experience

Interview questions from similar companies

Interview experience
4
Good
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Aptitude Test 

I was a test in our college of about 45min revolving around aptitude.

Round 2 - Coding Test 

Few basic coding questions.

Round 3 - One-on-one 

(2 Questions)

  • Q1. About linear and logistic regression
  • Q2. About svm and kernels
Interview experience
4
Good
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(1 Question)

  • Q1. Azure Data Lake, Prediction model
Interview experience
4
Good
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(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

  • Answered by AI
  • 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

  • Answered by AI
Round 2 - Technical 

(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...

  • Answered by AI

Skills evaluated in this interview

Interview experience
3
Average
Difficulty level
Moderate
Process Duration
2-4 weeks
Result
Selected Selected

I applied via Campus Placement and was interviewed before Dec 2023. There were 2 interview rounds.

Round 1 - Coding Test 

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.

Round 2 - Aptitude Test 

Normal aptitude questions

Interview Preparation Tips

Interview preparation tips for other job seekers - Focus on machine learning concepts, develop strong knowledge in Python programming, and learn about PCA, clustering, cross-validation, and hyperparameter tuning.

I applied via Job Portal and was interviewed in Dec 2021. There were 2 interview rounds.

Round 1 - Resume Shortlist 
Pro Tip by AmbitionBox:
Keep your resume crisp and to the point. A recruiter looks at your resume for an average of 6 seconds, make sure to leave the best impression.
View all tips
Round 2 - Technical 

(1 Question)

  • Q1. Metrics and related questions

Interview Preparation Tips

Interview preparation tips for other job seekers - Quite easy if you know ml basics
Interview experience
3
Average
Difficulty level
Moderate
Process Duration
4-6 weeks
Result
No response

I applied via Naukri.com and was interviewed in Mar 2024. There were 3 interview rounds.

Round 1 - One-on-one 

(3 Questions)

  • 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...

  • Answered by AI
  • Q2. Credit risk life cycle
  • Q3. Pandas related questions
Round 2 - One-on-one 

(3 Questions)

  • 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...

  • Answered by AI
  • Q2. Pandas related questions
  • Q3. Bagging and Boosting
Round 3 - One-on-one 

(3 Questions)

  • 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...

  • Answered by AI
  • 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.

  • Answered by AI
  • Q3. Bagging and boosting

Skills evaluated in this interview

Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
4-6 weeks
Result
Selected Selected

I applied via Naukri.com and was interviewed before May 2023. There were 2 interview rounds.

Round 1 - Aptitude Test 

Test was conducted on datacamp assessments. Overall, there were three tests.
1. Stats test
2. ML test
3. Python/coding test

Round 2 - One-on-one 

(1 Question)

  • Q1. Questions on ML techniques and practices, how to handle large data in python, lots of logical questions and handling overfitting, underfitting, etc in model building.

Interview Preparation Tips

Topics to prepare for HDFC Bank Data Scientist interview:
  • machine learning
  • python
Interview preparation tips for other job seekers - Learn about ML topics and commonly faced problems.
Interview experience
4
Good
Difficulty level
Moderate
Process Duration
-
Result
-
Round 1 - Technical 

(2 Questions)

  • Q1. It was more related to my projects
  • Q2. Asked questions on PCA,KNN and Elbow method .
Round 2 - Technical 

(2 Questions)

  • Q1. Asked SVM and Logistic regression, their applications and real life uses
  • Q2. Matrix formation of a knockout Tournament
Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Not Selected

I applied via Campus Placement and was interviewed in Oct 2024. There were 3 interview rounds.

Round 1 - Aptitude Test 

Asked questions of finance and aptitude

Round 2 - One-on-one 

(3 Questions)

  • Q1. What is machine learning?
  • Ans. 

    Machine learning is a branch of artificial intelligence that involves developing algorithms and models that enable computers to learn from and make predictions or decisions based on data.

    • Machine learning is a subset of artificial intelligence that focuses on developing algorithms that can learn from and make predictions or decisions based on data.

    • It involves training models on large datasets to recognize patterns and m...

  • Answered by AI
  • Q2. What do you know about SQL?
  • Ans. 

    SQL is a programming language used for managing and manipulating relational databases.

    • SQL stands for Structured Query Language

    • It is used to retrieve and manipulate data in relational databases

    • Common SQL commands include SELECT, INSERT, UPDATE, DELETE

    • SQL can be used to create tables, indexes, and views

    • Examples of SQL databases include MySQL, PostgreSQL, Oracle

  • Answered by AI
  • Q3. What you know about software Development?
  • Ans. 

    Software development involves creating, designing, testing, and maintaining software applications.

    • Software development includes coding, testing, debugging, and documenting software applications.

    • Developers use programming languages like Java, Python, C++, etc. to write code.

    • Agile and Waterfall are common software development methodologies.

    • Version control systems like Git are used to manage code changes.

    • Software developm...

  • Answered by AI
Round 3 - One-on-one 

(2 Questions)

  • Q1. What are the algorithms you know in machine learning and their details ?
  • Ans. 

    Various machine learning algorithms with brief details

    • Supervised Learning: Linear Regression, Logistic Regression, Support Vector Machines (SVM), Decision Trees, Random Forest

    • Unsupervised Learning: K-means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA)

    • Reinforcement Learning: Q-Learning, Deep Q-Networks (DQN)

    • Neural Networks: Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), L

  • Answered by AI
  • Q2. Python questions

Interview Preparation Tips

Interview preparation tips for other job seekers - Prepare Python and ML well.

Skills evaluated in this interview

TVS Credit Interview FAQs

How many rounds are there in TVS Credit Data Scientist interview?
TVS Credit interview process usually has 2 rounds. The most common rounds in the TVS Credit interview process are Aptitude Test and One-on-one Round.
How to prepare for TVS Credit Data Scientist interview?
Go through your CV in detail and study all the technologies mentioned in your CV. Prepare at least two technologies or languages in depth if you are appearing for a technical interview at TVS Credit. The most common topics and skills that interviewers at TVS Credit expect are Algorithms, Analytics, Data Analytics, Data Management and Data Science.

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TVS Credit Data Scientist Interview Process

based on 1 interview

Interview experience

4
  
Good
View more
TVS Credit Data Scientist Salary
based on 52 salaries
₹5 L/yr - ₹17 L/yr
21% less than the average Data Scientist Salary in India
View more details

TVS Credit Data Scientist Reviews and Ratings

based on 7 reviews

3.6/5

Rating in categories

3.6

Skill development

3.8

Work-life balance

3.3

Salary

3.6

Job security

3.6

Company culture

2.8

Promotions

3.0

Work satisfaction

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