Mastercard Data Scientist Interview Questions + Guide in 2024

Mastercard is a global technology company in the payments industry, renowned for its commitment to connecting and powering an inclusive, digital economy. Using secure data and networks, Mastercard’s innovative solutions aid individuals, financial institutions, governments, and businesses in realizing their greatest potential.

As a Data Scientist at Mastercard, your role will involve using advanced analytics and machine learning techniques to drive impactful business outcomes. You’ll be expected to demonstrate proficiency in Python, SQL, and various data science tools while effectively handling large datasets and complex queries. The position demands strong problem-solving skills, effective communication, and a readiness to work cross-functionally to develop and deploy data-driven solutions.

If you’re considering joining Mastercard, Interview Query has created this guide to help you navigate the interview process, common technical Mastercard data scientist interview questions, and provide essential preparation tips. Let’s get started!

The interview process usually depends on the role and seniority; however, you can expect the following on a Mastercard data scientist interview:

Recruiter/Hiring Manager Call Screening

If your CV is among the shortlisted few, a recruiter from the Mastercard Talent Acquisition Team will contact you and verify key details like your experiences and skill level. Behavioral questions may also be part of the screening process.

Sometimes, the Mastercard Data Scientist hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will invite you to the technical screening round. Technical screening for the Mastercard Data Scientist role is usually conducted virtually, including video conference and screen sharing. Questions in this 1-hour interview stage may revolve around Mastercard’s data systems, ETL pipelines, machine learning questions, SQL queries, and statistical analysis.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds will be conducted during your day at the Mastercard office, varying with the role. Your technical prowess, including programming and ML modeling capabilities, will be evaluated against the finalized candidates throughout these interviews.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the Data Scientist role at Mastercard.

Typically, interviews at Mastercard vary by role and team, but commonly, Data Scientist interviews follow a fairly standardized process across these question topics.

1. What are the drawbacks of organizing student test scores in the given layouts?

Assume you have data on student test scores in two different layouts. Identify the drawbacks of these layouts and suggest formatting changes to make the data more useful for analysis. Additionally, describe common problems seen in “messy” datasets.

2. How would you locate a mouse in a 4x4 grid using the fewest scans?

You have a 4x4 grid with a mouse trapped in one cell. You can scan subsets of cells to know if the mouse is within that subset. Describe a strategy to find the mouse using the fewest number of scans.

3. How would you select Dashers for Doordash deliveries in NYC and Charlotte?

Doordash is launching delivery services in New York City and Charlotte and needs a process for selecting dashers. Explain how you would decide which dashers to select and whether the criteria would be the same for both cities.

4. What factors could bias Jetco’s study on-boarding times?

A study showed that Jetco, a new airline, has the fastest average boarding times. Identify potential factors that could have biased this result and what you would investigate further.

5. How would you design an A/B test to evaluate a pricing increase for a B2B SAAS company?

A B2B SAAS company wants to test different subscription pricing levels. Describe how you would design a two-week A/B test to evaluate a pricing increase and determine if it is a good business decision.

6. Write a SQL query to select the 2nd highest salary in the engineering department.

Write an SQL query to select the second-highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

7. Write a function to find the maximum number in a list of integers.

Given a list of integers, write a function that returns the maximum number in the list. If the list is empty, return None.

8. Create a function convert_to_bst to convert a sorted list into a balanced binary tree.

Given a sorted list, create a function convert_to_bst that converts the list into a balanced binary tree. The output binary tree should have a height difference of at most one between the left and right subtrees of all nodes.

9. Write a function to simulate drawing balls from a jar.

Write a function to simulate drawing balls from a jar. The colors of the balls are stored in a list named jar, with corresponding counts of the balls stored in the same index in a list called n_balls.

10. Develop a function can_shift to determine if one string can be shifted to become another.

Given two strings A and B, write a function can_shift to return whether or not A can be shifted some number of places to get B.

11. How would you handle data preparation for building a machine learning model using imbalanced data?

When preparing data for a machine learning model with imbalanced classes, what steps would you take to address the imbalance and ensure the model performs well?

12. How much should a ride-sharing app budget for a $5 coupon initiative?

A ride-sharing app has a probability p of dispensing a $5 coupon to a rider and services N riders. Calculate the total budget needed for the coupon initiative.

13. What is a confidence interval for a statistic, and why is it useful?

Explain what a confidence interval is, why it is useful to know the confidence interval for a statistic, and how to calculate it.

14. What is the probability of finding an item on Amazon’s website?

Amazon has a warehouse system where items are located at different distribution centers. Given the probability that item X is available at warehouse A (0.6) or warehouse B (0.8), calculate the probability that item X would be found on Amazon’s website.

15. Is a coin that lands tails 8 out of 10 times fair?

You flip a coin 10 times, and it comes up tails 8 times and heads twice. Determine if this is a fair coin.

16. What are time series models and why are they needed?

Describe what time series models are and explain why they are necessary when less complicated regression models exist.

You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your Mastercard interview include:

1. Understand the Tech Stack: You should clearly understand the tech stack mentioned by the team. Ensure you’re comfortable using the tools in the Python data stack, especially numpy, pandas, scikit-learn, scipy, statsmodels. If you’re not familiar with any of these, take some time to practice.

2. SQL and Data Analysis: The team uses SQL for data analysis, so ensure your SQL skills are sharp. You should be able to write complex queries and understand how to analyze data using SQL.

3. Be Proficient with AWS Services: Brush up on your knowledge and understanding of AWS services, especially AWS Sagemaker, OpenSearch, Athena, Step Functions, EMR, and S3. If possible, try to get hands-on experience with these services.

What is the average salary for a Data Scientist at Mastercard?

What are the key skills needed for a Data Scientist at Mastercard?

Mastercard looks for proficiency in data science tools such as Python, SQL, and Hadoop, along with experience in machine learning libraries like sci-kit-learn, TensorFlow, and PyTorch. Strong analytical, problem-solving, and communication skills and the ability to work with big data and create innovative data solutions are essential.

What kind of projects will I work on as a Data Scientist at Mastercard?

As a Data Scientist at Mastercard, you will work on projects that include analyzing the effectiveness of product offerings, building fraud detection models, and creating ML solutions for business problems. You’ll have the opportunity to collaborate with teams across different functions, work with large datasets, and translate analysis into impactful business decisions.

What is Mastercard’s work culture like?

Mastercard strives to create an inclusive culture that respects and values diverse perspectives and experiences. They emphasize a collaborative environment, innovation, and continuous learning. The company drives sustainable and inclusive growth and supports employees through various developmental and well-being programs.

Mastercard is not just a technology leader in the payments industry; it is a mission-driven company dedicated to fostering an inclusive digital economy. Their Data Scientist roles are meticulously designed to harness the power of data and innovation to create safer, smarter, and more accessible transactions, impacting global commerce.

If you want more insights about the company, check out our main Mastercard Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about Mastercard’s interview process for different positions.

You can also check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!