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MasterCard Data Science Interview Questions

Company: Mastercard
Position: Data Scientist
Location: Gurugram
Salary: 12LPA – 30LPA

Mastercard Incorporated, headquartered in Purchase, New York, is a global leader in financial services. Known for its innovative payment solutions and commitment to financial inclusion, Mastercard operates in over 210 countries and territories. If you’re preparing for a Data Science role at Mastercard, here’s a detailed breakdown of their interview process and the types of questions you can expect.

Mastercard Data Science Interview Questions.

Interview Process

The Mastercard Data Science interview process typically consists of 5 rounds, each designed to evaluate different aspects of your technical and analytical skills:

Round 1 – Telephonic Screening

Focus: Basic understanding of Data Science concepts, SQL, and Python/R.
Format: You’ll be asked to explain your projects and solve a few coding or SQL problems.

Round 2 – Walk-in/Face-to-Face Technical Round

Focus: Advanced SQL, coding, and problem-solving.
Format: You’ll solve problems on a whiteboard or shared document.

Round 3 – Project Analysis

Focus: Deep dive into your past projects.
Format: You’ll be asked to explain your approach, tools used, and the impact of your work.

Round 4 – Case Studies

Focus: Business problem-solving and data-driven decision-making.
Format: You’ll be given a real-world scenario and asked to propose solutions.

Round 5 – Hiring Manager Round

Focus: Cultural fit, communication skills, and long-term career goals.
Format: Behavioral questions and high-level discussions about your experience.

Difficulty of Questions

SQL – 9/10

1) How can you find employees who have never taken leave from the leaves table?

SELECT e.name
FROM employees e
LEFT JOIN leaves l ON e.id = l.employee_id
WHERE l.employee_id IS NULL;

2) How can you find the most recently hired employee?

SELECT name, joining_date
FROM employees
ORDER BY joining_date DESC
LIMIT 1;

3) How can you count the number of orders placed by each customer from an orders table?

SELECT customer_id, COUNT(*) AS total_orders
FROM orders
GROUP BY customer_id;

4) How can you find customers who have never placed an order?

SELECT c.name
FROM customers c
LEFT JOIN orders o ON c.id = o.customer_id
WHERE o.customer_id IS NULL;

5) How can you find the employee with the second-highest experience based on joining_date?

SELECT name, joining_date
FROM employees
ORDER BY joining_date ASC
LIMIT 1 OFFSET 1;

R/Python – 7/10

1) Write a Python function that takes a string as input and returns a list of all email addresses found in the string, using the re module.

2) Write a Python function that takes two date strings in the format “YYYY-MM-DD” as input and returns the number of days between them.

3) Write a Python function that checks if a directory exists.

4) Write a Python function that makes a GET request to a given URL and returns the status code of the response.

5) Write a Python function that creates and starts a new thread that prints “Hello from thread!” after a short delay.

Statistics/ML

1) State one situation where the set-based solution is advantageous over the cursor-based solution.

A set-based solution is preferred when handling large data operations efficiently.

Example: Updating salaries for all employees in a company.

• Set-Based Approach: UPDATE Employee SET Salary = Salary * 1.10 (executes in one go).
• Cursor-Based Approach: Fetch each employee row, update salary, and commit changes (slow and inefficient).

Conclusion: Set-based solutions are faster and more scalable than cursors for bulk operations.

2) Design a recommendation engine from end to end from a dataset to deployment in production.

To build a recommendation system:

1. Data Collection – Gather user interactions, purchase history, ratings.
2. Data Preprocessing – Clean, normalize, and remove inconsistencies.
3. Feature Engineering – Create useful features like user preferences, purchase frequency.
4. Model Selection – Choose between:
   • Collaborative Filtering (User-User, Item-Item)
   • Content-Based Filtering (based on product descriptions, user interests)
5. Training and Evaluation – Train on historical data, evaluate using Precision, Recall, RMSE.
6. Deployment – Use cloud platforms like AWS, Google Cloud, or Flask APIs for real-time recommendations.
7. Continuous Monitoring – Track user behavior and update recommendations dynamically.

Example: Netflix recommends shows based on viewing history using collaborative filtering.

3) Explain what metrics we should use to evaluate a binary classification model.

Key metrics for evaluating a binary classification model:

1. Accuracy – Measures overall correctness: (TP + TN) / (TP + TN + FP + FN).
2. Precision – How many predicted positives are correct: TP / (TP + FP).
3. Recall (Sensitivity) – How many actual positives are detected: TP / (TP + FN).
4. F1-Score – Harmonic mean of precision and recall: 2 × (Precision × Recall) / (Precision + Recall).
5. ROC-AUC Score – Measures model’s ability to distinguish between classes.

Example: In spam detection, high recall is important to catch all spam emails, even if some false positives occur.

4) How can you design a product recommendation system based on taxonomy?

A taxonomy-based recommendation system organizes products into categories and subcategories.

Steps:

1. Define Taxonomy – Group products based on attributes (e.g., Electronics → Phones → Smartphones).
2. User Profile Matching – Match user preferences to taxonomy-based categories.
3. Content-Based Filtering – Recommend similar products within the same taxonomy.
4. Hybrid Approach – Combine taxonomy-based filtering with collaborative filtering for better recommendations.

Example: Amazon suggests “Smartphones” when a user buys “Wireless Earbuds” using category-based recommendations.

5) A person using a search engine needs to find something. How do you come up with an algorithm that will predict what the user needs after they type only a few letters?

To predict a user’s search intent based on partial input, we can use Auto-Complete Algorithms:

1. N-gram Models – Suggest words based on previous search patterns.
2. Trie Data Structure – Efficiently stores and retrieves prefix-based suggestions.
3. Machine Learning (ML) Models – Train on past searches to predict relevant queries.
4. Personalization – Use a user’s past search history for customized predictions.
5. Real-time Updates – Continuously improve suggestions based on trending searches.

Example: Google suggests “Python tutorial” when a user types “Py” based on N-gram frequency analysis.

Case Study

Problem Statement:

Mastercard wants to improve its fraud detection system by identifying fraudulent transactions in real-time while minimizing false positives. Your task as a data scientist is to analyze transaction data, detect suspicious patterns, and develop strategies to enhance fraud prevention.

Dataset Overview:

You have access to a dataset containing past transaction records with fraud labels. The dataset includes the following attributes:

Dataset Overview:

You have access to a dataset containing past transaction records with fraud labels. The dataset includes the following attributes:

• Transaction_ID – Unique identifier for each transaction
• User_ID – Unique identifier for the cardholder
• Transaction_Amount – Amount spent in the transaction
• Merchant_Category – Type of merchant (e.g., grocery, electronics, luxury, travel)
• Transaction_Location – Location where the transaction occurred
• Transaction_Timestamp – Date and time of the transaction
• Device_Type – Device used for the transaction (mobile, desktop, ATM, POS)
• IP_Address – IP address of the transaction source
• Previous_Fraud_Flag – Whether the user has reported fraud before (1 = Yes, 0 = No)
• Transaction_Status – 1 if the transaction is fraudulent, 0 if legitimate

Key Questions to Answer:

1. What are the key indicators of fraudulent transactions?

• Do fraudsters prefer certain transaction locations or merchant categories?
• Are there patterns in transaction amounts that indicate fraud?
• Does the frequency of transactions within a short time frame help detect fraud?

2. How can Mastercard improve fraud detection?

• Should Mastercard use rule-based models or machine learning algorithms for fraud detection?
• Can real-time anomaly detection reduce fraud while maintaining transaction speed?
• How can external factors like device type and IP address enhance fraud detection models?

3. What strategies can Mastercard implement to minimize false positives?

• How can Mastercard balance fraud detection and user experience?
• Should Mastercard use dynamic risk scoring instead of blocking transactions outright?
• How can Mastercard refine fraud prevention using historical user behavior analysis?

Key Insights & Business Recommendations

1. Identifying Key Fraud Patterns

• Unusual Transaction Amounts: Transactions that deviate significantly from a user’s normal spending behavior are often fraudulent.
• Geographical Mismatch: If a card is used in two different countries within a short time frame, it may indicate fraud.
• Device and IP Address Anomalies: Fraudsters often use new devices and VPNs to hide their locations, making these key fraud indicators.
• High-Frequency Transactions: Multiple transactions in a short time span, especially at high-risk merchants (e.g., electronics stores, luxury retailers), can indicate fraud.

2. Enhancing Fraud Detection Models

• Machine Learning-Based Risk Scoring: Implementing an AI-driven fraud detection system that continuously learns from new fraud patterns.
• Real-Time Anomaly Detection: Using unsupervised learning models (e.g., autoencoders, clustering) to detect unusual spending behavior.
• Behavioral Biometrics: Analyzing how users interact with payment platforms (e.g., typing speed, mouse movements) to detect fraudulent behavior.
• Two-Factor Authentication (2FA) Triggers: Requiring extra verification for high-risk transactions based on spending behavior and location.

3. Minimizing False Positives for Better Customer Experience

• Personalized Risk-Based Authentication: Instead of outright rejecting transactions, flagging them for step-up authentication (e.g., OTP verification, biometric confirmation).
• Dynamic Fraud Thresholds: Adjusting fraud detection thresholds based on user history to prevent blocking legitimate transactions.
• Real-Time Customer Alerts: Notifying users of potentially fraudulent transactions and allowing them to verify or dispute them instantly.

Basic, you can practice a lot of case studies and other statistics topics here –
https://thedatamonk.com/data-science-resources/