American Express Data Scientist Interview Questions + Guide in 2024

Founded in 1850 as a freight forwarding company in the US, American Express has since evolved into a global services leader. Today, they offer customers access to a wide range of products, insights, and experiences that enhance lives and drive business success.

As a data scientist in American Express, they play a critical role in leveraging data to drive business growth, manage risk, and enhance customer satisfaction.

In this guide, we will walk you through some of the frequently asked American Express data scientist interview questions, along with tips to help you stand out and make a lasting impression to your future employers.

Phone Screening

The process often begins with a phone call with a recruiter who will discuss your background, the role, and your interest in American Express. They may also ask basic questions about your experience, skills, and expectations.

Onsite Interviews (or Virtual Onsite)

This round includes technical, live coding, and/or case study interviews conducted by a team member, typically a senior data scientist. In addition to the technical questions, you might be asked some behavioral interview questions to see culture fit.

Hiring Manager Round

The final interview involves a conversation with the hiring manager or a senior leader. This round focuses on cultural fit, your motivations, and how you align with the company’s values and goals. Expect to be asked technical questions as well.

Here are a few questions that get asked in American Express data scientist interviews:

1. Why do you want to join American Express?
2. What’s a random forest?
3. How do you handle and avoid overfitting?
4. Based on the customer spending data, how would you determine what our next partner card should be?
5. You’re a credit card company with 100K small businesses as potential partners but can only reach out to 1,000. How would you prioritize which businesses to contact?
6. Write a SQL query to select the 2nd highest salary in the engineering department.
7. What’s the difference between bagging vs boosting?
8. If a logistic model is heavily weighted on a variable, and some values in that variable have lost their decimal points (e.g., 100.00 becomes 10000), is the model still valid? Why or why not, and how would you fix it?
9. Write a query to show the number of users, number of transactions placed, and total order amount per month in the year 2020.
10. Write a SQL query to calculate the total expenditure for each department in 2022.
11. Write a Python function max_profit that takes a list of stock prices and returns the maximum profit from one buy and one sell.
12. Your co-worker develops a model that takes in customer inputs and returns if a loan should be given or not. What metrics would you track to measure the success of the new model?
13. How would you build a credit card fraud detection model?
14. You notice that the credit card payment amount per transaction has decreased. How would you investigate what happened?
15. How would you prioritize multiple deadlines?
16. How would you resolve a conflict between your colleague?
17. Given a list of integers, write a function that returns the maximum number in the list. If the list is empty, return None.
18. Write a query to get the total three-day rolling average for deposits by day.
19. Given two tables: employees and projects, find the sum of the salaries of all the employees who didn’t complete any of their projects.
20. Given a dataset, how would you analyze the dataset to understand exactly where the revenue loss is occurring?

Here are some tips to help you excel in your upcoming American Express data scientist interview:

Understand the Role and Company

Research about American Express and familiarize yourself with their product, partnerships, and any challenges they face in the finance industry

Brush Up Your Technical Skills

Study key data science concepts like statistics, machine learning algorithms, and data manipulation, with a focus on languages like Python, R, and SQL.

Practice solving case studies

Practice solving data science case studies relevant to American Express’ business problems such as customer segmentation or credit risk assessment.

Showcase Soft Skills

Prepare to articulate your ability to communicate complex technical concepts to non-technical stakeholders, collaborate effectively with cross-functional teams, and solve business challenges efficiently. Practice using the STAR method to structure your responses in behavioral interviews.

Do Mock Interview with Peers

Nothing beats practicing for data scientist interviews with another person to get real-time feedback! Use our P2P Mock Interview Portal and AI Interviewer to conduct mock interviews with friends or fellow candidates. Focus on clear and concise communication to receive constructive feedback on your responses and refine them for your upcoming American Express data scientist interview.

What’s the average annual salary for a data scientist at American Express?

The salary for American Express data scientists ranges between $70k to $143K, with an average of $111K, depending on location and job role.

What other companies are hiring data scientist besides American Express?

Numerous companies are hiring Data Scientists across various industries. Some well-known examples include Google, JPMorgan Chase, and Amazon

Does Interview Query have job postings for American Express data science roles?

Yes, visit our Job Board to check out current opportunities.

While the American Express data scientist interview process can be demanding, we hope this guide provides valuable support. Best of luck on your journey!