Mastercard is a global technology company in the payments industry, dedicated to building an inclusive, digital economy that benefits everyone by providing secure, simple, and smart transaction solutions. Our innovative efforts aim to drive growth and make a positive societal impact globally.

Joining Mastercard as a Data Analyst means stepping into a role that requires keen analytical skills and a consultative approach. In this position, you will be responsible for conducting sophisticated quantitative analyses, creating data-driven insights, and supporting various teams in refining personalization strategies. Expect to engage in a multi-round interview process involving technical assessments, behavioral questions, and discussions on past projects.

This guide by Interview Query will walk you through the interview stages, from typical screening questions to advanced problem-solving puzzles, ensuring you're well-prepared for each step. Let’s get started!

Submitting Your Application

The initial step to attaining the Data Analyst position at Mastercard is to submit a well-crafted application that highlights your technical skills and your enthusiasm for joining Mastercard. Review the job description thoroughly and tailor your resume to align with the specified prerequisites. It is advantageous to incorporate relevant keywords that hiring managers may look for and to write a targeted cover letter showcasing pertinent skills and experiences.

Recruiter/Hiring Manager Call Screening

Once your resume is shortlisted, a recruiter from Mastercard’s Talent Acquisition Team will reach out to validate key details about your experiences and skill set. This initial screening call will generally include a mix of behavioral questions and questions based on the STAR technique, emphasizing Situation, Task, Action, and Result from your experiences.

During this round, which typically lasts about 30 minutes, be prepared for questions about your background and why you are interested in working for Mastercard.

Technical Virtual Interview

Passing the initial recruiter screening will lead you to the technical virtual interview round. This phase encompasses virtual video conferencing and screen sharing sessions. These 1-hour technical interviews focus on your proficiency with SQL, data systems, and your understanding of statistics.

Expect questions revolving around statistical analysis, data handling proficiency, guesstimates, and specific technical queries about past projects listed on your resume. There may also be practical exercises, including case studies and database design questions, to assess your problem-solving and technical skills.

Onsite Interview Rounds

The onsite interview stage at Mastercard involves multiple interview rounds with different stakeholders, including department managers and tech leads. These interviews will delve deeper into your work experience, technical abilities, and behavioral aspects. Discussing real-life scenarios, puzzles, guesstimates, and SQL questions are common.

If you had taken a technical assignment previously, you might also have a presentation round based on your solutions. The interviews will often touch on your ability to work within teams, manage conflicts, and understand your motivations for applying to Mastercard.

Quick Tips for MasterCard Data Analyst Interviews

A few tips for acing your Mastercard interview include:

• Highlight Your Quantitative Skills: Mastercard places a strong emphasis on candidates' ability to perform sophisticated quantitative analyses. Brush up on your knowledge of statistics, probability, and data visualization tools like Tableau.
• Master the Behavioral Interview: Many rounds include behavioral questions based on the STAR approach. Prepare to discuss your past experiences in detail, focusing on challenges, your roles, and the outcomes.
• Understand Mastercard’s Core Values: Mastercard's organizational culture emphasizes inclusiveness, digital innovation, and social responsibility. Your answers should reflect these values, particularly when responding to behavioral questions.

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

• What are the drawbacks of having student test scores organized 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.

• 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 of the cells. 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.

• How would you select Dashers for Doordash deliveries in NYC and Charlotte? Doordash is launching delivery services in New York City and Charlotte. Describe the process for selecting Dashers (delivery drivers) and discuss whether the criteria for selection should be the same for both cities.

• What factors could bias Jetco's study on boarding times? Jetco, a new airline, has the fastest average boarding times according to a study. Identify potential factors that could have biased this result and explain what you would investigate further.

• 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-long A/B test to evaluate a pricing increase and determine if it is a good business decision.

• Write a SQL query to select the 2nd highest salary in the engineering department. Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

• 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.

• 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.

• 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.

• Develop a function can_shift to check 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.

• - 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?

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

• What is the probability of riders getting a coupon in a ride-sharing app? A driver using the app picks up two passengers. Determine:

• The probability of both riders getting the coupon.

• The probability that only one of them will get the coupon.

• 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.

• What is the probability of finding an item on Amazon's website given warehouse availability? Amazon has a warehouse system where items are located at different distribution centers. In one city, the probability that item X is available at warehouse A is 0.6 and at warehouse B is 0.8. Calculate the probability that item X would be found on Amazon's website.

• Is a coin fair if it comes up tails 8 times out of 10 flips? You flip a coin 10 times, and it comes up tails 8 times and heads twice. Determine if this coin is fair.

• What are time series models and why are they needed? Describe what time series models are and explain why they are needed when less complicated regression models exist.

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

Q: What skills are essential for a Data Analyst position at Mastercard?

A: Essential skills for a Data Analyst at Mastercard include strong analytical and problem-solving skills, proficiency in tools like Google Analytics, Adobe Analytics, SQL, and Tableau. You should have excellent verbal and written communication skills and a consultative approach with a customer-centric mindset. Knowledge of eCommerce technology and digital consultation or analytics experience is a plus.

Q: What does the interview process for a Data Analyst at Mastercard look like?

A: The interview process typically involves multiple rounds, starting with an initial HR screening to discuss your background and experience. This is followed by technical interviews with questions ranging from statistical queries to detailed discussions on past projects. The process might also include aptitude, logical reasoning, and verbal tests, ending with an HR interview discussing preferences and company fit.

Q: What is Mastercard's company culture like?

A: Mastercard values a culture of inclusion, respect for individual strengths, views, and experiences. The company believes in making transactions safe, simple, smart, and accessible, fostering an environment where innovation thrives. The "Decency Quotient" or DQ drives the company culture, emphasizing decency and collaboration both inside and outside of the organization.

Q: What kind of projects can you expect as a Data Analyst at Mastercard?

A: As a Data Analyst at Mastercard, you will work on leveraging client data to create actionable insights and personalized strategies. Projects often involve performing quantitative analyses, building and analyzing reports, and collaborating with various teams like Sales, Marketing, and Product to drive business growth for clients.

Q: How can I prepare for the Data Analyst interview at Mastercard?

A: To prepare for the interview, research Mastercard’s mission and values, and familiarize yourself with common data analysis tools and techniques. Practice problem-solving with logical puzzles and guesstimates, and review your past projects to discuss them in detail. Utilize Interview Query to practice potential interview questions and refine your technical and soft skills.

The interview process for the Data Analyst position at Mastercard is both comprehensive and structured, ensuring that candidates are thoroughly assessed for both technical and behavioral competencies. From initial screenings involving STAR-based questions to technical interviews focusing on SQL, statistical knowledge, and project specifics, applicants are tested on various fronts. The rigorous process includes aptitude tests, logical puzzles, and multiple interview rounds with HR, technical leads, and managers. Mastering these stages requires a good grasp of quantitative analyses, data interpretation, and communication skills.

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.

At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every Mastercard data analyst interview question and challenge.

You can 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!