American Express Data Analyst Interview Questions + Guide in 2024
Overview
American Express is a leading global financial services corporation known for its commitment to providing exceptional customer experiences and innovative financial products. As one of the world’s most respected brands, Amex offers an inclusive and dynamic work environment, consistently recognized for its dedication to diversity, equity, and integrity. With its rich history and strong emphasis on community support, American Express continues to set the standard in the financial services industry.
This guide will walk you through the interview stages, common American Express data analyst interview questions, and tips for success. Let’s get started on your journey to becoming part of the American Express family!
What Is the Interview Process Like for a Data Analyst Role at American Express?
The interview process usually depends on the role and seniority, however, you can expect the following on a American Express data analyst interview:
HR Phone Screening
After your application is shortlisted, an HR representative will contact you for an initial phone screening. This step usually covers:
- Introduction and summary of your experience.
- Assessment of your technical skills in Python, SQL, and other relevant tools (typically rated on a scale from 1 to 5).
- Discussion on your compensation expectations.
This round typically lasts about 30 minutes and sets the stage for the subsequent interviews.
Panel Interview with Managers
If you pass the HR screening, you will be invited to a panel interview. This round includes:
- Introduction and detailed discussion about your project experiences, including specifics like data models and cleaning processes.
- One probability or statistics question.
- One SQL query focusing on window functions.
- 1-2 business case studies to evaluate your problem-solving and business acumen.
The panel interview lasts for approximately 45 minutes to an hour. Managers here could be potential team members and provide more in-depth insights into the role and organizational culture.
Technical Interview
The next step is a technical interview, focusing on your proficiency in SQL, Python, and other necessary tools. It includes:
- Basic to intermediate SQL and Python-based questions.
- Scenario-based problems like data cleaning and transformation within a limited timeframe.
- Puzzles and guesstimates.
- Expressions of interest and questions surrounding American Express’s business model.
This interview assesses your technical capabilities and your practical problem-solving skills.
Onsite Interview Rounds
After successfully navigating the previous rounds, you may be invited for onsite interviews. These rounds involve:
- Multiple interviews with different managers and team members.
- Thorough evaluation of your technical and analytical skills through coding challenges, SQL queries, and data visualization tasks.
- Presentation from the take-home exercises (if assigned).
- Real-life business problems to assess your industry knowledge and innovative thinking.
Expect a follow-up call from the recruiter detailing the next steps if you successfully navigate through the rounds. Remember, the entire process evaluates both your technical prowess and your cultural fit within the organization.
What Questions Are Asked in an American Express Data Analyst Interview?
Typically, interviews at American Express vary by role and team, but commonly Data Analyst interviews follow a fairly standardized process across these question topics.
1. How would you determine what our next partner card should be?
You have access to all customer spending data. How would you analyze this data to decide on the best partner for a new credit card, similar to Starbucks or Whole Foods Chase credit cards?
2. What are the Z and t-tests, and when should you use each?
Explain the Z and t-tests, their uses, differences, and the scenarios in which one should be used over the other.
3. How would you build a strategy to find the best businesses to reach out to?
As a credit card company looking to partner with more merchants, you have 100K small businesses to choose from but can only contact 1,000. How would you strategize to identify the best businesses to reach out to?
4. 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.
Note: If more than one person shares the highest salary, the query should select the next highest salary.
Example:
Input:
employees table
| Column | Type |
|---|---|
id |
INTEGER |
first_name |
VARCHAR |
last_name |
VARCHAR |
salary |
INTEGER |
department_id |
INTEGER |
departments table
| Column | Type |
|---|---|
id |
INTEGER |
name |
VARCHAR |
Output:
| Column | Type |
|---|---|
salary |
INTEGER |
5. What’s the difference between Lasso and Ridge Regression?
Explain the key differences between Lasso and Ridge Regression, focusing on their regularization techniques and how they handle coefficients.
6. When would you use a bagging algorithm versus a boosting algorithm?
Compare two machine learning algorithms. Describe scenarios where you would prefer a bagging algorithm over a boosting algorithm and discuss the tradeoffs between the two.
7. Is a logistic model valid if a key variable has data quality issues?
Assume a logistic model heavily relies on one variable, which has data quality issues (e.g., decimal points removed). Discuss whether the model remains valid and how you would fix it.
8. What is the difference between XGBoost and random forest algorithms?
Explain the differences between XGBoost and random forest algorithms. Provide an example of a situation where you would choose one over the other.
9. Does increasing the number of trees in a random forest always improve accuracy?
If you sequentially increase the number of trees in a random forest model, will the accuracy continue to increase? Discuss the impact on model performance.
10. What are the Z and t-tests, and when should you use each?
Explain what Z-tests and t-tests are, their purposes, and the differences between them. Specify scenarios where one test is preferred over the other.
How to Prepare for a Data Analyst Interview at American Express
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 American Express data analyst interview include:
- Research American Express Products and Services: Be familiar with key products and services offered by American Express and understand their competitive landscape. This could help in framing your business cases and aligning your responses with the company’s objectives.
- Practice SQL and Python: Brush up on your SQL and Python skills. Familiarize yourself with common queries, data manipulation techniques, and statistical functions, as technical questions form a significant part of the interview process.
- Prepare for Behavioral Questions: Be ready to share experiences related to teamwork, problem-solving, and how you handle challenges. Practice articulating these experiences clearly, focusing on the outcomes and learnings.
FAQs
What is the average salary for a Data Analyst at American Express?
Average Base Salary
Average Total Compensation
What skills are essential for a Data Analyst at American Express?
Key skills include proficiency in SQL and Python, experience with data visualization tools like Tableau, and a good understanding of statistical methods and machine learning. Strong communication and problem-solving skills are also crucial.
How does American Express ensure a smooth candidate experience during the interview process?
American Express aims to provide a supportive interview experience with friendly and accommodating interviewers who give ample time for responding to questions. However, feedback and communication post-interview can sometimes be delayed, as shared in some candidate experiences.
Why should I consider a career as a Data Analyst at American Express?
American Express offers a dynamic and inclusive work environment with excellent benefits, professional growth opportunities, and a supportive culture that values your contributions. Joining Team Amex allows you to work on impactful analytics projects and be part of a global community dedicated to customer and employee success.
Conclusion
If you’re preparing for an American Express interview, be sure to showcase your expertise in data analysis, SQL, and Python, and highlight your ability to seamlessly integrate into their innovative culture. Remember, every step is a unique opportunity to demonstrate your skills and passion for the role.
If you want more insights about the company, check out our main American Express Interview Guide, where we have covered many interview questions that could be asked. Additionally, explore our interview guides for other roles such as software engineer and business analyst to learn more about Expedia’s interview process for different positions.
Good luck with your interview!