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Hero FinCorp Data Scientist Interview Questions and Answers

Updated 28 May 2024

Hero FinCorp Data Scientist Interview Experiences

1 interview found

Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Campus Placement and was interviewed in Nov 2023. There were 5 interview rounds.

Round 1 - Technical 

(2 Questions)

  • Q1. Interview was related to work exp, Internship and project.
  • Q2. They asked questions on random forest and PCA
Round 2 - Coding Test 

2 DSA easy to medium question in python language.

Round 3 - Behavioral 

(2 Questions)

  • Q1. Discussion on why you want to join company?
  • Q2. Discussion on Projects
Round 4 - Technical 

(1 Question)

  • Q1. Classical ML questions
Round 5 - HR 

(1 Question)

  • Q1. 45 minute hr round

Interview questions from similar companies

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 was 1 interview round.

Round 1 - Technical 

(4 Questions)

  • Q1. Explain any Data Science project
  • Ans. 

    Developed a predictive model to forecast customer churn for a telecommunications company.

    • Identified key features such as customer tenure, monthly charges, and service usage

    • Collected and cleaned data from customer databases

    • Built a machine learning model using logistic regression or random forest algorithms

    • Evaluated model performance using metrics like accuracy, precision, and recall

    • Provided actionable insights to reduce

  • Answered by AI
  • Q2. Types of Error in Statistics
  • Ans. 

    Types of errors in statistics include sampling error, measurement error, and non-sampling error.

    • Sampling error occurs when the sample does not represent the population accurately.

    • Measurement error is caused by inaccuracies in data collection or measurement instruments.

    • Non-sampling error includes errors in data processing, analysis, and interpretation.

    • Examples: Sampling error - selecting a biased sample, Measurement err...

  • Answered by AI
  • Q3. Types of Machine learning models
  • Ans. 

    Types of machine learning models include supervised learning, unsupervised learning, and reinforcement learning.

    • Supervised learning: Models learn from labeled data, making predictions based on past examples (e.g. linear regression, support vector machines)

    • Unsupervised learning: Models find patterns in unlabeled data, clustering similar data points together (e.g. k-means clustering, PCA)

    • Reinforcement learning: Models le...

  • Answered by AI
  • Q4. Functions of pandas library, such as get_dummies()
  • Ans. 

    get_dummies() function in pandas library is used to convert categorical variables into dummy/indicator variables.

    • get_dummies() function creates dummy variables for categorical columns in a DataFrame.

    • It converts categorical variables into numerical representation for machine learning models.

    • Example: df = pd.get_dummies(df, columns=['column_name'])

  • Answered by AI

Interview Preparation Tips

Interview preparation tips for other job seekers - On campus interview, Be confident, be good at project explaination.

Skills evaluated in this interview

I applied via Naukri.com and was interviewed in Jan 2021. There were 3 interview rounds.

Interview Questionnaire 

3 Questions

  • Q1. Questions were mainly based on past role and work experience
  • Q2. Have you worked on customer segmentation?
  • Ans. 

    Yes, I have worked on customer segmentation.

    • I have used clustering algorithms like K-means and hierarchical clustering to segment customers based on their behavior and demographics.

    • I have also used decision trees and random forests to identify the most important features for segmentation.

    • I have experience with both supervised and unsupervised learning techniques for customer segmentation.

    • I have worked on projects where...

  • Answered by AI
  • Q3. In depth interview on tools worked upon

Interview Preparation Tips

Interview preparation tips for other job seekers - Give genuine answers of you have not worked on any of the tools feel free to tell the same. That won't affect your interview.
Interview experience
4
Good
Difficulty level
Moderate
Process Duration
4-6 weeks
Result
Selected Selected

I applied via Recruitment Consulltant and was interviewed before May 2023. There were 2 interview rounds.

Round 1 - Technical 

(2 Questions)

  • Q1. Machine learning algorithms
  • Ans. 

    Machine learning algorithms are used to analyze data and make predictions or decisions 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 learn from data to improve their performance over...

  • Answered by AI
  • Q2. SQL basics and basic knowledge of security products
Round 2 - HR 

(1 Question)

  • Q1. Very lengthy process and delayed a lot

Skills evaluated in this interview

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
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 - 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
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

Hero FinCorp Interview FAQs

How many rounds are there in Hero FinCorp Data Scientist interview?
Hero FinCorp interview process usually has 5 rounds. The most common rounds in the Hero FinCorp interview process are Technical, Coding Test and Behavioral.
How to prepare for Hero FinCorp 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 Hero FinCorp. The most common topics and skills that interviewers at Hero FinCorp expect are Data Science, Python, Analytics, Hive and Predictive Analytics.
What are the top questions asked in Hero FinCorp Data Scientist interview?

Some of the top questions asked at the Hero FinCorp Data Scientist interview -

  1. Interview was related to work exp, Internship and proje...read more
  2. They asked questions on random forest and ...read more
  3. Classical ML questi...read more

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Hero FinCorp Data Scientist Interview Process

based on 2 interviews

Interview experience

5
  
Excellent
View more
Hero FinCorp Data Scientist Salary
based on 23 salaries
₹8.6 L/yr - ₹30 L/yr
19% more than the average Data Scientist Salary in India
View more details

Hero FinCorp Data Scientist Reviews and Ratings

based on 2 reviews

3.8/5

Rating in categories

2.8

Skill development

3.9

Work-life balance

3.7

Salary

4.0

Job security

3.7

Company culture

2.9

Promotions

3.7

Work satisfaction

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