American Express - Machine Learning Engineer Interview Guide

American Express, a leading global service company, is renowned for its commitment to backing customers, communities, and employees. Joining Team Amex offers the chance to be part of a diverse tech team, playing a pivotal role in shaping the digital lives of its customers.

As a Machine Learning Engineer at American Express, you will be part of an innovative R&D team, leveraging your Data Science and Engineering skills to explore cutting-edge technologies. You will be involved in crafting, coding, and implementing solutions to challenging problems, bringing sophisticated analytical techniques into production to drive business innovations.

In this guide from Interview Query, we’ll walk you through the interview process, frequently asked questions, and offer valuable tips to help you prepare effectively for this exciting opportunity at American Express. Let's dive in!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining American Express as a Machine Learning Engineer. Whether you were contacted by an American Express recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the American Express Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

In some cases, the American Express hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the Machine Learning Engineer role at American Express is usually conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around American Express’s data systems, ETL pipelines, and your proficiency with programming languages such as Python or Java, SQL/NoSQL databases, and machine learning frameworks like TensorFlow or ScikitLearn.

In the case of the Machine Learning Engineer role, take-home assignments regarding machine learning model development, data analytics, and system design may be incorporated. Apart from these, your proficiency against hypothesis testing, probability distributions, and other technical skills may also be assessed during the round.

Depending on the seniority of the position, case studies and similar real-scenario problems may also be assigned.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds, varying with the role, will be conducted during your day at the American Express office. Your technical prowess, including programming and ML modeling capabilities, will be evaluated against the finalized candidates throughout these interviews.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the Machine Learning Engineer role at American Express.

Quick Tips For American Express Machine Learning Engineer Interviews

• Should contain three tips for interviewing for this specific company based on interview experiences.

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 interview include:

• Understand Business Impact: American Express values insights that drive business decisions. Make sure to articulate how your machine learning models and data analysis can have a tangible impact on business processes and customer experience.
• Hands-on Proficiency: Demonstrate your coding and technical skills effectively. During the technical rounds, be prepared to write and debug machine learning algorithms, review code, and discuss your approach towards solving real-world problems.
• Behavioral and Situational Awareness: American Express values cultural fit and teamwork. Be ready to discuss past experiences where you worked as part of a team, faced challenges, and how you embody the company values in your professional conduct.

Typically, interviews at American Express vary by role and team, but commonly Machine Learning Engineer interviews follow a fairly standardized process across these question topics.

• 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?

• What are the Z and t-tests, and when should you use each? Explain the Z and t-tests, their uses, differences, and scenarios where one is preferred over the other.

• How would you build a strategy to find the best businesses to reach out to? As a credit card company with limited manpower, you need to select 1,000 out of 100K small businesses to partner with. How would you develop a strategy to identify the best candidates?

• 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.

• 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.

• 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.

• 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.

• 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.

• 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 improve? Discuss the impact on model performance.

• 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.

What is the average salary for a Machine Learning Engineer at American Express?

Q: What is the interview process like for a Machine Learning Engineer at American Express? The interview process begins with a programming task and a quiz. If you pass, you will have a video conference round with the recruiter followed by technical interviews. These interviews focus on your resume, the tasks you've accomplished, and your expertise in domain-specific queries, particularly in NLP.

Q: What responsibilities does a Senior Machine Learning Engineer have at American Express? The role involves spending over 70% of your time coding and experimenting with novel solutions to challenging problems. You will combine data science and engineering skills to develop scalable machine learning solutions, put models into production, and integrate insights into operational workflows. Additionally, you'll participate in global tech communities and conferences, and contribute to technology roadmaps and scientific publications.

Q: What skills and qualifications are required for the Machine Learning Engineer position? Candidates should have a Master's or PhD in a relevant field, 5+ years of tech experience, and strong programming skills in Python and/or Java, SQL/NoSQL, and popular ML technologies like TensorFlow and Scikit-Learn. Expertise in specialized areas such as NLP, Deep Learning, and Reinforcement Learning, along with experience in building and deploying scalable ML applications, is highly preferred.

Q: What is the company culture like at American Express? At American Express, you become part of a global and diverse community committed to backing customers and colleagues. The company emphasizes recognition for contributions, leadership, and integrity. You will find a supportive, inclusive environment where your voice is valued, and there's a strong focus on continuous learning and professional development.

Q: How does American Express support the professional growth of its tech employees? American Express offers dedicated time for professional development and encourages participation in open-source communities. The Technology Community Office fosters a collaborative culture, supporting internal Engineering Guilds, attending conferences, and providing comprehensive resources to help technologists thrive and innovate.

Conclusion

Joining the American Express team as a Machine Learning Engineer presents a unique opportunity to contribute to meaningful projects in a dynamic and inclusive environment. At American Express, your skills will be recognized and nurtured, providing you with ample opportunities for professional growth and development.

You will work on cutting-edge technologies and be at the forefront of innovation, shaping the future of the industry alongside talented engineers. The commitment to community, teamwork, and individual recognition makes American Express an ideal workplace for those who thrive on collaboration and creativity.

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. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about American Express’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 American Express machine learning engineer 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!