Visa Data Scientist Interview Questions + Guide in 2024

Founded in 1958 by Bank of America, Visa Inc. is the second-largest card payment service globally. It operates in over 200 countries and processes billions of transactions annually.

Visa Inc. has issued over 4.3 billion Visa cards worldwide. Each time a payment is made with a Visa card, data is generated. With millions of transactions daily, the company handles immense amounts of data, which presents significant career opportunities for data professionals.

We’ve compiled this guide to help you understand Visa’s hiring process and offer tips. We will also cover some common questions and guidance on answering them.

The interview process for a data science role at Visa is carefully structured to assess your business acumen, machine learning expertise, and behavioral skills. Before the interview, your recruiter will provide preparation documents containing sample questions to help you prepare. In total, there are four rounds, each with a specific focus. Below is a detailed description of each round.

Recruiter’s screening

First, a recruiter will review your application and conduct an initial interview to check your suitability for the role. This could be a phone or video interview, during which they will discuss your background, skills, and experience. Their goal is to make sure you meet the basic qualifications and are a good fit for the position before moving on to the next stages of the interview process.

Technical Interview

In the next round, you’ll have a technical interview with a senior data scientist. They’ll ask questions related to your skills listed on your resume, focusing on SQL, Python, machine learning knowledge, and model performance. This round isn’t difficult if you’re well-prepared. Additionally, you’ll have the opportunity to discuss your previous projects and experience.

Online Assessment or Case Study

After the technical interview, you will proceed to a HackerRank assessment or a case study. During the HackerRank assessment, you will encounter technical questions or coding challenges related to SQL, Python, and machine learning concepts. In some cases, you may be presented with a case study that simulates real-world data science problems encountered by Visa.

Super Day

The final round, often referred to as “Super Day,” typically takes place on-site and consists of up to five back-to-back interviews, each lasting 45 minutes. During Super Day, expect a variety of interview formats, including behavioral questions, discussions about the role and team, and insights into Visa’s current projects. Additionally, the panel may pose technical questions or coding challenges to assess your skills and suitability for the position.

In this section, we’ll delve into a selection of questions commonly posed during Visa data science interviews or ones that could be asked. Expect a blend of behavioral and technical questions spanning various domains such as SQL, Python, statistics and probability, big data, and machine learning.

1. What do you know about Visa Inc.?

Your prospective interviewer wants to see if you have done your homework, researched Visa, and learned about its products and services. They want to know if you are genuinely interested in working there as a data scientist.

How to Answer

Provide a brief overview of the company, its history, products, services, and its position in the market.

Example

“Visa Inc. is a global payments technology company that facilitates electronic funds transfers worldwide. Founded in 1958, Visa operates one of the world’s largest retail electronic payments networks, connecting millions of businesses, financial institutions, governments, and consumers in more than 200 countries and territories. Visa provides payment solutions, including credit cards, debit cards, prepaid cards, and digital payment services. The company aims to enable individuals, businesses, and economies to thrive by providing secure, convenient, and reliable payment solutions.”

2. Why do you want to work at Visa?

The interviewer wants to make sure that new hires are really interested in the job and plan to stay with Visa for a long time.

How to Answer

This is your chance to showcase your understanding of the company, its values, and how your skills align with its mission.

Example

“I want to work at Visa because I am passionate about the intersection of technology and finance, and Visa is at the forefront of innovation in the payments industry. I admire Visa’s commitment to providing secure and convenient payment solutions that empower businesses and consumers worldwide. Additionally, I am impressed by Visa’s culture of collaboration, diversity, and inclusion, which aligns with my own values. I am excited about the opportunity to contribute my skills and experience to Visa’s mission of enabling individuals, businesses, and economies to thrive through digital payments.”

3. How would you handle a recurring situation where a coworker consistently arrives late to scheduled weekly meetings?

The interviewer is interested in how you handle conflicts, find solutions, and maintain good relationships with coworkers. They also want to see how you approach problem-solving, communication, and teamwork.

How to Answer

When answering, it’s essential to provide a structured response showcasing your effective approach to resolving conflicts. Start by acknowledging tardiness and its impact on team productivity. Then, explain how you would approach the coworker privately.

Example

“If faced with a recurring situation where a coworker consistently arrives late to scheduled meetings, I would first approach the coworker privately to discuss the issue. I would express the importance of punctuality and how their tardiness affects the productivity of the team and the success of the meetings. I would then ask if there are any underlying reasons for their lateness and explore potential solutions together, such as adjusting the meeting time or providing reminders. If the issue persists, I would talk to our supervisor for further advice and support in resolving the situation in a friendly way.”

4. Describe a data project you worked on. What were some of the challenges you faced?

They’re interested in your hands-on experience with data projects, problem-solving approach, and how you’d manage a project. They also want to gauge your work ethic and how you handle stress and challenges.

How to Answer

When answering, provide a brief overview of the data project you worked on, including its objectives and scope. Then, discuss the challenges you faced during the project. Finally, explain how you addressed these challenges and what strategies you used to overcome them.

Example

“One data project I worked on involved analyzing transaction data to identify fraudulent patterns and improve fraud detection systems. One of the main challenges we encountered was dealing with a large volume of unstructured transaction data from multiple sources, which required extensive data cleaning and preprocessing. Additionally, we faced issues with imbalanced datasets, where fraudulent transactions were rare compared to legitimate ones, making it challenging to train accurate predictive models. To address these challenges, we implemented advanced data-cleaning techniques and applied resampling methods to balance the dataset. We also collaborated closely with domain experts to better understand fraud patterns and iteratively refine our models.”

5. Could you share an experience where you received negative feedback?

The interviewer might ask this question at a Visa data science interview to test your ability to handle constructive criticism and your willingness to learn and grow from feedback.

How to Answer

When answering, it’s important to be honest about a time when you received negative feedback and describe how you used it as an opportunity for growth and improvement.

Example

“One instance where I received negative feedback was during a data analysis project where my initial approach to data visualization was criticized for being overly complex. Instead of going on the defensive, I accepted the feedback and revisited my approach. I simplified the visualizations and incorporated feedback from stakeholders to make sure everything was clear and usable. As a result, the revised visualizations were well-received, and the project outcome improved.”

6. How would you determine the location of a mouse trapped in one of the cells of a 4x4 grid using the fewest number of scans?

This question could be asked at your Visa data science interview to assess your problem-solving skills and your ability to think analytically.

How to Answer

You could propose a binary search approach, where you divide the grid into two equal halves and scan each half to determine which half contains the mouse. You would then repeat this process with the identified half.

Example

“One approach to determining the mouse’s location in the 4x4 grid would be to employ a binary search strategy. I would start by dividing the grid into two equal halves, either horizontally or vertically, and scanning each half to determine which half contains the mouse. I would then repeat this process with the identified half until we narrow down the search to a single cell. By iteratively halving the search space, I can locate the mouse using the fewest number of scans.”

7. What is the purpose of the “self” keyword in Python?

The employer wants to assess your understanding of Python programming fundamentals, essential for data manipulation, analysis, and modeling for data science projects at Visa.

How to Answer

When answering, you could explain that the “self” keyword in Python is used to refer to the current instance of a class, allowing you to access the attributes and methods of that class within its own scope.

Example

“The purpose of the ‘self’ keyword in Python is to represent the instance of a class. When defining methods within a class, including the init method which serves as the constructor, ‘self’ is used as the first parameter to refer to the current instance of the class. This allows you to access and modify the attributes and methods of that instance within the class’s scope. For example, when calling methods on an instance of the class, such as instance.method(), the ‘self’ parameter is implicitly passed, allowing the method to operate on the specific instance.”

8. Write a query to return the two students with the closest SAT test scores and the score difference. If multiple students have the same minimum score difference, select the student name combination that comes first alphabetically.

Visa processes a vast amount of transactional data from its global network of merchants and financial institutions, and SQL is often used to extract, transform, and analyze this data efficiently. The interviewer wants to test your SQL querying skills.

How to Answer

Write an SQL query that selects the two students with the closest SAT scores and calculates their score difference. You would also need to handle cases where multiple students have the same minimum score difference by selecting the student name combination that comes first alphabetically.

Example

SELECT 
    s1.student_name AS student1,
    s2.student_name AS student2,
    ABS(s1.sat_score - s2.sat_score) AS score_difference
FROM 
    students s1
JOIN 
    students s2
ON 
    s1.student_id < s2.student_id
ORDER BY 
    score_difference, student1, student2
LIMIT 
    1;

“This query selects all possible combinations of student pairs and calculates the absolute difference in SAT scores between them. It then orders the results by score difference and student names and selects the top row using the LIMIT clause. If there are multiple pairs with the same minimum score difference, the pair with the first alphabetically sorted student names is selected.”

9. How would you detect and remove outliers in a time series dataset?

Visa processes vast amounts of transactional data over time. Identifying and removing outliers is important for ensuring the quality of analytical insights derived from this data. The employer can ask you this question to assess your ability to preprocess and clean time series data.

How to Answer

To answer this question, you would explain a systematic approach to detecting and removing outliers in a time series dataset involving techniques such as visual inspection, statistical methods, Z-score method, and moving average.

Example

“To detect and remove outliers in a time series dataset, I would start by visually inspecting the data through plots such as line or scatter plots to identify any noticeable deviations from the overall trend. Then, I would calculate summary statistics such as mean, median, and standard deviation to quantify the data dispersion. Next, I might use techniques like the Z-score method to flag data points with extreme values or apply a moving average to smooth the data and highlight outliers. Additionally, I would consider domain-specific filters or rules to identify outliers that are inconsistent with the expected behavior of the data.”

10. How would you evaluate a model that predicts whether a piece of news is relevant when shared on X?

This question could be asked at a Visa data science interview because Visa, like many companies, may have a vested interest in understanding the relevance of news shared on social media platforms like X.

How to Answer

Describe a systematic approach to evaluating performance. Mention techniques such as splitting the data, choosing evaluation metrics, cross-validation, and confusion matrix analysis.

Example

“To evaluate a model that predicts the relevance of news shared on X, I would start by splitting the dataset into training, validation, and test sets. Then, I would train the model on the training set and evaluate its performance on the validation set using various evaluation metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. Additionally, I would perform k-fold cross-validation to assess the model’s generalization performance and ensure robustness. Furthermore, I would analyze the confusion matrix to understand the model’s performance in terms of true positives, false positives, true negatives, and false negatives. Finally, I would plot the ROC curve and precision-recall curve to visualize the model’s discrimination ability and select an appropriate threshold for classification.”

11. Explain the difference between UNION and UNION ALL in SQL and provide use cases for each.

Visa deals with lots of transaction data. Knowing SQL commands like UNION and UNION ALL is important for handling and analyzing this data. So, the interviewer might ask about these commands to see if you understand how to work with big data sets.

How to Answer

Explain the difference: UNION merges the results of multiple queries and removes duplicates, whereas UNION ALL combines results, including duplicates. Then, mention use cases for each.

Example

“UNION” is typically used when we want to merge the results of multiple queries and ensure that only distinct rows are returned. For instance, at Visa, we might use UNION to combine transaction data from different sources or databases while ensuring that we have a unique set of transactions for analysis. On the other hand, UNION ALL is useful when we want to combine the results of multiple queries and retain all rows, including duplicates. This could be beneficial when dealing with datasets where duplicates are expected or when removing duplicates would be inefficient.”

12. Let’s say you have a categorical variable with thousands of distinct values. How would you encode it?

Visa likely deals with various categorical variables in their data, such as merchant names, transaction types, or geographic regions. Encoding categorical variables is important for machine learning models to understand and process these features effectively.

How to Answer

Start by explaining different encoding techniques, such as one-hot encoding, label encoding, and binary encoding. Suggest using a combination of these techniques and feature engineering to address the challenge.

Example

“When faced with a categorical variable containing thousands of unique values, we must choose an encoding method carefully. While one-hot encoding creates binary columns for each category, it can lead to high dimensionality and sparse matrices. Label encoding assigns numerical values to categories, but may introduce unintended ordinal relationships. A better approach might involve a mix of techniques: using one-hot encoding for common categories and grouping less frequent ones into an ‘other’ category to manage dimensionality and noise. Additionally, using feature engineering can extract valuable insights, like frequency or similarity metrics.”

13. Write an SQL query to find the top 5 customers with the highest transaction amounts.

The employer may ask this question at your Visa data science interview because understanding how to extract insights from transactional data is important for various analytical tasks at Visa, such as identifying high-value customers or detecting anomalies.

How to Answer

To find the top 5 customers with the highest transaction amounts, query the transaction data, aggregate the amounts for each customer, and select the top 5 based on transaction amounts.

Example

SELECT customer_id, SUM(amount) AS total_transaction_amount
FROM transactions
GROUP BY customer_id
ORDER BY total_transaction_amount DESC
LIMIT 5;

14. Write an SQL query using the database schema to determine the percentage of transactions that include purchases of drinks along with meals.

Visa may be interested in this type of query to gain insights into consumer behavior and preferences. Knowing how to solve problems like this is essential for market analysis, trend identification, and targeted marketing campaigns.

How to Answer

First, join relevant tables containing transaction details and item descriptions, then filter for transactions that include both meal and drink items.

Example

WITH meal_transactions AS (
    SELECT transaction_id
    FROM transactions
    WHERE item_category = 'meal'
),
drink_transactions AS (
    SELECT transaction_id
    FROM transactions
    WHERE item_category = 'drink'
)
SELECT 
    (COUNT(DISTINCT mt.transaction_id) * 100.0) / NULLIF(COUNT(DISTINCT dt.transaction_id), 0) AS percentage
FROM meal_transactions mt
JOIN drink_transactions dt ON mt.transaction_id = dt.transaction_id;

15. Explain the difference between shallow copy and deep copy in Python.

The interviewer is evaluating your understanding of Python concepts since they are widely used in data science for data analysis, preprocessing, and modeling.

How to Answer

Explain the key differences between the two concepts, focusing on how they handle nested objects and their impact on memory management.

Example

“Shallow copy creates a new object but shares references to nested objects, whereas deep copy creates independent copies of all nested objects recursively. For instance, consider a scenario where we have a nested dictionary representing customer transactions. Using a shallow copy may lead to unintended modifications in the original data when manipulating the copied object. However, employing a deep copy ensures that any changes made to the copied data do not affect the original dataset.”

16. You’re given two tables: employees and managers. Find the names of all employees who joined before their manager.

This question tests your understanding of SQL. It also evaluates your ability to manipulate data using relational databases.

How to Answer

Use SQL JOIN operations to combine the employee and manager tables based on a common key, such as manager IDs. Apply a condition to filter for employees who joined before their managers, comparing their respective join dates.

Example

SELECT e.employee_name
FROM employees e
JOIN managers m ON e.manager_id = m.manager_id
WHERE e.join_date < m.join_date;

17. What are the challenges of training deep learning models on large-scale datasets?

Deep learning models are increasingly used in data science tasks at Visa, including fraud detection, customer segmentation, and anomaly detection. This question checks your understanding of the challenges of training deep learning models.

How to Answer

Describe common challenges associated with training deep learning models on large-scale datasets, such as computational resources, memory constraints, training time, overfitting, and data preprocessing.

Example

“Training deep learning models on large-scale datasets poses the following challenges. First, the demand for significant computational resources is essential to efficiently process immense volumes of data. Also, memory constraints become pronounced when loading large datasets into memory, and the prolonged training times associated with deep learning models can delay model deployment. Overfitting is another concern especially with extensive data, requiring the implementation of regularization techniques like dropout or weight decay to enhance model generalization.”

18. Given an annual_payments table. How many transactions listed as “paid” have an amount greater or equal to 100?

Data analysis tasks related to transaction data are fundamental to Visa’s operations. This question tests your SQL querying skills.

How to Answer

Use a SELECT statement to count the number of transactions listed as “paid” and have an amount greater than or equal to 100. Run the SQL query against the annual_payments table to retrieve the desired result.

Example

SELECT COUNT(*)
FROM annual_payments
WHERE payment_status = 'paid' AND amount >= 100;

19. What is one-hot encoding, and when would you use it in data preprocessing?

At Visa, data scientists tackle diverse tasks ranging from fraud detection and customer segmentation to trend analysis. This question delves into your grasp of fundamental data preprocessing techniques, which are pivotal for refining data before it undergoes analysis or model training.

How to Answer

Provide a clear definition of one-hot encoding. Explain situations where it is necessary, then highlight its benefits.

Example

“One-hot encoding is a technique used to represent categorical variables as binary vectors, where each category is represented by a binary digit (0 or 1). This method is employed in data preprocessing when dealing with categorical variables that cannot be directly used in machine learning algorithms. For instance, in Visa’s transaction data, categorical variables like transaction types or merchant categories need to be converted into numerical format for analysis and model training. One-hot encoding preserves the information contained in these categorical variables without introducing ordinal relationships, enabling machine learning algorithms to interpret them effectively.”

20. Let’s say we’re comparing two machine learning algorithms. In which case would you use a bagging algorithm versus a boosting algorithm?

This question might come up at Visa to see how well you know various machine learning algorithms and when to use them for tasks like fraud detection or customer segmentation.

How to Answer

Clearly explain bagging and boosting algorithms and describe scenarios where each would be a good fit.

Example

“When comparing two machine learning algorithms, the choice between using a bagging algorithm versus a boosting algorithm depends on the nature of the data and the task at hand. Bagging algorithms, like random forest, are suitable for scenarios where we need to reduce variance and improve generalization performance. On the other hand, boosting algorithms, such as AdaBoost or Gradient Boosting, are beneficial when we aim to improve the performance of weak learners iteratively. In customer segmentation tasks, boosting algorithms could be employed to enhance the classification accuracy of the model by focusing on instances that are frequently misclassified.”

The data science interview at Visa is manageable if you’re well-prepared. Take time to go through interview preparation materials provided by your recruiter. Here are some tips to help you excel in your Visa interview:

Understand the Company

Familiarize yourself with Visa’s key products, including credit cards, debit cards, prepaid cards, and digital payment solutions. Research recent news and initiatives related to Visa’s data science efforts, such as advancements in artificial intelligence, machine learning, and data-driven decision-making. Be prepared to discuss how your analytical skills and domain expertise can contribute to these initiatives.

Follow our data science learning path for guidance on learning crucial concepts.

Practice Coding

Practice coding in SQL, Python, and machine learning to sharpen your skills and prepare for the interview.

At Interview Query, you can access our extensive question bank to practice coding questions. Filter questions by difficulty level and company to focus on areas you want to improve.

Practice Behavioral Questions

Practice answering behavioral questions to effectively communicate your experiences and skills in interviews. Reflect on situations where you demonstrated key qualities such as leadership, problem-solving, teamwork, and adaptability. Use the STAR method (Situation, Task, Action, Result) to structure your responses and showcase your capabilities confidently.

Check out our Top 30 Data Science Behavioral Interview Questions to prepare for behavioral questions.

Stay Updated

Stay in the loop with trends in fintech and data science so you’re aware of the latest developments and can adapt your strategies accordingly to remain competitive and innovative in the industry.

Follow our blog, where we regularly publish articles related to data science, to stay updated and gain valuable insights to help you stay ahead of the curve.

Mock Interviews

Mock interviews are a great way to practice and improve your interview skills in a simulated environment.

Practice mock interviews at Interview Query to build confidence, refine your responses, and identify areas for improvement before the real interview.

What is the average salary for a data science role at Visa?

The average base salary for a data scientist role at Visa is $112,892. The estimated average total compensation is $152,408.

For deeper insights into general data science salaries, check out our comprehensive data science salary guide.

What other companies are hiring data scientists besides Visa?

Other fintech companies are also hiring data scientists. You can explore opportunities at PayPal, Square, Stripe, Capital One, and Robinhood. For more insights into these companies and others, check out our company interview guides.

Does Interview Query have job postings for the Visa data scientist role?

Explore our job board to find current openings for data scientist roles at Visa and other companies. Filter based on seniority level, location, and other criteria to find positions that align with your preferences and qualifications.

For more comprehensive preparation, explore our collection of Visa interview questions. We’ve also covered other positions at Visa, such as data analyst, data engineer, and software engineer. Check them out if you’re interested!

Additionally, we have other resources regarding data science interviews. Dive into our top 25+ Data Science SQL Interview Questions, 9 Data Science Project Interview Questions, and The Best Data Science Interview Books. For practical challenges, check out our Top 20 Data Science Take-home Challenges, and The Ultimate SQL Cheat Sheet for quick reference.

Remember, the data science job market is highly competitive in 2024, and if you’ve secured an interview, you’ve already stood out from thousands of applicants. Now, focus on diligently preparing and performing your best to ace the interview.

Best of luck!