Mastercard Interview Questions

Position – Full-time Job

Profile – Associate Analyst/Consultant

Process – Mastercard shortlisted the C.V.s and 3 Rounds of Interviews followed

Interview Questions

Round 1 

1. Tell me about yourself
2. Why did you go for Masters in Statistics and not Operational Research even though the coursework of the former is theoretical and that of the latter is technical and application-oriented?
3. Are you comfortable with Python syntaxes?
4. How will you find all the unique values in a column?
5. How will you rename a column?
6. What are various ways to combine two datasets?
7. How will you load a dataset which is too large in size to hold in memory?
8. How will you create a new column whose value is calculated from two other columns?
9. What is the difference between loc() and iloc()?
10. How will you calculate the total number of null values in a dataset?
11. In a dataset, there is a sex column with the following unique values – Male, Female, M, F, other. How will you change these values to make this column consistent with a supervised model?
12. In a dataset with an age column, extract all those rows with ages between 18 and 60.
13. What is the use of .append() and .extend() on a list?
14. What is the difference between Linear and Logistic regression?

• Can we apply linear regression instead of logistic regression and vice versa?

15. Puzzle – Given two hourglass of 4 and 7 minutes respectively, measure 9 minutes.

Round 2 

1. Walk me through your CV.
2. Give a detailed description of your project.
3. Which technique did you use to fill the null values and why?
4. Differentiate between supervised and unsupervised learning.
5. What are the various metrics used to measure the model performance of a logistic regression model?

• Can we rely on accuracy alone to measure model performance? If not, why?
•  What do you understand by sensitivity and specificity?

6. Why did you scale your data?
7. What is Ridge Regression? When is it used?
8. What features other than the ones already used in the project can affect the target variable?
9. What can be done to improve models’ ability to generalize better to unseen data?
10. Guesstimate – Suppose a new city like Faridabad is emerging, calculate the total number of ATMs that would be required. Give a detailed explanation of your approach.
11. Have you worked with SQL?
12. How do you deal with categorical data in a supervised model?

Round 3

1. Describe yourself as a person.
2. Tell me about your experience in the past few months.
3. One thing that you miss most about college? 
4. The biggest challenge that you have faced and how did you overcome it?
5. What do you know about Mastercard?
6. What comes to your mind when you think of a corporate workspace?
7. Where do you see yourself in the next 5 years?

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