American Express Data Scientist Interview Questions & Experience Guide
Company Name: American Express
Position: Data Scientist
Application Process: Applied through the company’s career portal.
Interview Experience:
- On-Site Interview:
- Details: The interview process was conducted on-site. Unfortunately, the company did not reimburse travel expenses as promised, which was a significant inconvenience.
- Outcome: No feedback or rejection notice was provided post-interview. Follow-up inquiries about travel reimbursement were ignored by HR.
Warning for Future Candidates:
- Be cautious about traveling for interviews with American Express, as they may not honor reimbursement promises.
- HR responsiveness post-interview was extremely poor, leaving candidates in the dark about their status.
Conclusion:
The overall experience was disappointing due to the lack of professionalism in handling reimbursements and communication post-interview. Candidates should proceed with caution and clarify all terms before committing to travel.
Company Name: American Express
Position: Data Scientist
Location: [Location not specified]
Application Process: [Details not provided]
Interview Rounds:
-
Round 1 - Technical Interview:
- Questions Asked: Mostly Python-related questions and logical questions specific to chatbot development and deployment on cloud platforms.
- Your Approach: Focused on demonstrating proficiency in Python and understanding of chatbot workflows, including deployment strategies.
- Outcome: The interviewers were clear about their expectations, and the round went smoothly.
-
Round 2 - [Round Type]:
- Questions Asked: [Details not provided]
- Your Approach: [Details not provided]
- Outcome: [Details not provided]
Preparation Tips:
- Brush up on Python, especially for chatbot-related tasks.
- Understand the basics of deploying chatbots on cloud platforms.
- Practice logical problem-solving related to chatbot workflows.
Conclusion:
The interview process was smooth, and the interviewers were well-prepared. The role was very specific, so focusing on chatbot development and Python helped. For future candidates, ensure you have a strong grasp of Python and chatbot deployment concepts.
Company Name: American Express
Position: Data Scientist
Application Process: Applied through the company’s career portal.
Interview Rounds:
- Round 1 - Technical Interview:
- Questions Asked:
- A coding question (could be solved in any language).
- Data science questions:
- How to handle a situation where you have lesser data for a certain category?
- How to cope with overfitting?
- Your Approach:
- For the coding question, I chose Python and explained my thought process while solving it.
- For the data science questions, I discussed techniques like data augmentation, resampling, and using synthetic data for imbalanced datasets. For overfitting, I talked about regularization, cross-validation, and pruning in decision trees.
- Outcome: Cleared the round and moved to the next stage.
- Questions Asked:
Preparation Tips:
- Brush up on coding problems, especially in Python or your preferred language.
- Revise core data science concepts like handling imbalanced data, overfitting, and model evaluation techniques.
- Practice explaining your thought process clearly during problem-solving.
Conclusion:
The interview was quite interactive, and the interviewer was helpful. I felt confident about my answers, but I could have prepared more real-world examples for the data science questions. For future candidates, focus on both coding and conceptual clarity in data science topics.
Company Name: American Express
Position: Data Scientist
Application Process: I applied through an online application process.
Interview Rounds:
- Round 1 - Phone Interview:
- Questions Asked:
- What are the stages to graduation?
- How proficient are you in machine learning and coding?
- Your Approach: I answered the questions honestly, explaining my academic journey and highlighting my skills in machine learning and coding.
- Outcome: The interviewer was friendly, and the conversation went well. I passed this round.
- Questions Asked:
Preparation Tips:
- Be clear and concise about your academic background and technical skills.
- Practice explaining your proficiency in machine learning and coding in a way that aligns with the role.
Conclusion:
The phone interview was a smooth experience. The interviewer was very approachable, which made it easier to communicate. I would advise future candidates to be confident and articulate about their skills and experiences.
Company Name: American Express
Position: Data Scientist
Location: [Location (if applicable)]
Application Process: [Brief description of how the student applied]
Interview Rounds:
-
Round 1 - Behavioral and Technical Interview:
-
Questions Asked:
- Behavioral questions focused on teamwork, problem-solving, and past experiences.
- Technical questions involved writing code by hand to demonstrate logic.
-
Your Approach:
- For behavioral questions, I used the STAR method to structure my answers.
- For the coding part, I focused on explaining my thought process clearly while writing the logic.
-
Outcome: [Result of this round]
Preparation Tips:
- Practice writing code by hand to get comfortable with the format.
- Review behavioral questions and prepare answers using the STAR method.
- Brush up on fundamental data science concepts and algorithms.
Conclusion:
The interview was a good mix of behavioral and technical challenges. I felt prepared for the coding part but could have practiced more behavioral scenarios. For future candidates, focus on clear communication and logical structuring of answers.
Company Name: American Express
Position: Data Scientist
Location: On-site (Location not specified)
Application Process: The application process details were not provided, but the candidate attended an on-site interview.
Interview Rounds:
- Round 1 - On-site Interview:
- Questions Asked: The interview lasted approximately 5 hours, during which the candidate met with 5 different interviewers. The questions were not specified, but the interviewers discussed the company culture extensively.
- Your Approach: The candidate engaged positively with the interviewers, showing enthusiasm about the company culture and the role.
- Outcome: The interviewers were pleasant and left the candidate excited about the prospect of working at American Express.
Conclusion:
The overall experience was very positive, with the interviewers effectively conveying the company’s culture and values. The candidate felt more excited about the opportunity after the interview. While specific technical or behavioral questions weren’t mentioned, the interaction highlighted the importance of cultural fit and enthusiasm during the interview process.
Company Name: American Express
Position: Data Scientist
Application Process: The process began with a coffee chat and information session, which was very insightful. The representatives were kind and provided a lot of details about the role and career paths at the company.
Interview Rounds:
- Round 1 - Coffee Chat & Information Session:
- Questions Asked: The session focused on understanding the company culture, the role of a Data Scientist at American Express, and the training provided. There were no technical questions; it was more about aligning expectations and learning about the company.
- Your Approach: I actively listened, asked questions about the work environment, and shared my enthusiasm for the role.
- Outcome: The session was very positive, and I felt more confident about the company and the role.
Preparation Tips:
- Research the company’s values and culture beforehand to ask relevant questions during the coffee chat.
- Be genuine and show enthusiasm for the role and the company’s mission.
Conclusion:
The experience was fantastic! The representatives were very helpful, and the company’s focus on training and customer obsession stood out. I would advise future candidates to engage actively during such sessions and express their genuine interest in the company.
Company Name: American Express
Position: Data Scientist
Location: [Location not specified]
Application Process: [Details not provided]
Interview Rounds:
-
Round 1 - Phone Interview:
-
Questions Asked: [Details not provided]
-
Your Approach: [Details not provided]
-
Outcome: [Details not provided]
-
Round 2 - Phone Interview:
-
Questions Asked: [Details not provided]
-
Your Approach: [Details not provided]
-
Outcome: [Details not provided]
-
Round 3 - Onsite Interview:
-
Questions Asked: Met with 5 team members/managers 1-on-1 and had a one-hour presentation.
-
Your Approach: The interactions were pleasant, and the team members were nice.
-
Outcome: [Details not provided]
Preparation Tips:
[No tips provided]
Conclusion:
The onsite interview experience was pleasant, and the team members were welcoming. [Further details not provided]
Company Name: American Express
Position: Data Scientist
Application Process: I passed the initial screening and was invited for the interview process.
Interview Rounds:
-
Round 1 - Technical Interview:
- Questions Asked: The questions were focused on technical skills relevant to the Data Scientist role, though specific details weren’t provided.
- Your Approach: I prepared by reviewing core data science concepts, algorithms, and problem-solving techniques.
- Outcome: I moved on to the next round, but no specific feedback was given.
-
Round 2 - Behavioral Interview:
- Questions Asked: The questions were behavioral, likely assessing fit and soft skills.
- Your Approach: I tried to answer using the STAR method, focusing on clear examples from my past experiences.
- Outcome: Unfortunately, I didn’t receive any feedback, so I assumed I didn’t pass this round.
Conclusion:
The experience was straightforward, but the lack of feedback made it hard to pinpoint areas for improvement. For future candidates, I’d recommend preparing thoroughly for both technical and behavioral rounds and asking for feedback if possible.
Company Name: American Express
Position: Data Scientist
Location: Desert Ridge office
Application Process: The interview took place at the Desert Ridge office. The environment was very welcoming, and the team was kind. There were two interviewers for the first part and one for the technical part. I also had the opportunity to meet many other employees during the process.
Interview Rounds:
-
Round 1 - Initial Interview:
-
Questions Asked: General questions about my background, experience, and interest in the role. Some behavioral questions were also included to assess fit.
-
Your Approach: I focused on highlighting my relevant experience and how it aligns with the role. For behavioral questions, I used the STAR method to structure my answers.
-
Outcome: Passed to the next round.
-
Round 2 - Technical Interview:
-
Questions Asked: Technical questions related to data science, including problem-solving scenarios, coding challenges, and discussions about past projects.
-
Your Approach: I tackled the coding challenges by breaking them down into smaller steps and explaining my thought process. For project discussions, I emphasized the impact of my work and the tools I used.
-
Outcome: Awaiting results.
Preparation Tips:
- Reviewed common data science concepts and practiced coding problems.
- Prepared for behavioral questions using the STAR method.
- Researched the company and its projects to tailor my answers.
Conclusion:
Overall, it was a great experience. The interviewers were friendly, and the questions were fair. I felt well-prepared, but I could have practiced more coding problems to feel even more confident. My advice to future candidates is to thoroughly review both technical and behavioral aspects and to stay calm and composed during the interview.
Company Name: American Express
Position: Data Scientist
Application Process: I received an email about the position through university emails. I sent my resume, and within an hour, they provided timeslots for the interview along with a conference call number and passcode.
Interview Rounds:
- Round 1 - Technical Interview:
- Questions Asked:
- Basic machine learning questions (specific questions not mentioned).
- Your Approach: I solved the first question correctly and the second one almost correctly.
- Outcome: It seemed they had already chosen their candidate before my interview, which felt unfair given my qualifications compared to the selected candidate.
- Questions Asked:
Conclusion:
The interview process was quick, but it was disappointing to learn they might have already finalized their candidate beforehand. Despite having relevant publications, the outcome wasn’t favorable. For future candidates, it’s important to stay prepared but also be aware that sometimes decisions are made before the interview.
Company Name: American Express
Position: Data Scientist
Location: [Location (if applicable)]
Application Process: The interview was conducted on Webex. The interviewer was the senior manager in the company. We started our interviews by introducing ourselves to each other. Then, he asked me questions from my resume.
Interview Rounds:
-
Round 1 - Technical Interview:
-
Questions Asked: Questions were primarily based on my resume, focusing on my past projects, skills, and experiences.
-
Your Approach: I ensured I was thoroughly familiar with every detail on my resume and could explain my projects and contributions clearly. I also prepared to discuss any technical challenges I faced and how I resolved them.
-
Outcome: The round went well, and the interviewer seemed satisfied with my responses.
Preparation Tips:
-
Review your resume in detail and be prepared to discuss every project or experience listed.
-
Practice explaining your technical contributions and problem-solving approaches clearly and concisely.
-
Brush up on any tools or technologies mentioned in your resume to answer follow-up questions confidently.
Conclusion:
The interview was a great learning experience. I felt well-prepared for the resume-based questions, but I could have spent more time anticipating deeper technical follow-ups. My advice to future candidates is to not only know your resume inside out but also be ready to dive deep into any technical aspect of your work.
Company Name: American Express
Position: Data Scientist
Application Process: I applied through the company’s career portal after coming across the job posting. The process was straightforward, and I received a response within a couple of weeks.
Interview Rounds:
- Round 1 - Technical Discussion:
- Questions Asked: The interviewer started by discussing the projects listed on my resume in detail. They asked about the methodologies I used, the challenges I faced, and the outcomes of these projects. This was followed by a technical question related to a Kaggle problem in the retail domain. The task was to solve the problem using Machine Learning algorithms, with a preference for the KNN method.
- Your Approach: For the resume discussion, I focused on explaining the problem statements, my thought process, and the impact of my solutions. For the Kaggle question, I walked through my understanding of the retail problem, why KNN might be suitable, and how I would implement it step-by-step, including data preprocessing and model evaluation.
- Outcome: I passed this round and was invited for further discussions.
Preparation Tips:
- Brush up on your resume projects thoroughly, as they are often the starting point for technical discussions.
- Practice explaining your thought process clearly, especially for open-ended problems like Kaggle challenges.
- Revisit fundamental ML algorithms like KNN, as interviewers might ask you to apply them to real-world scenarios.
Conclusion:
Overall, the interview was a great learning experience. The interviewer was very engaged and provided constructive feedback. I realized the importance of being able to articulate my ideas clearly and concisely. For future candidates, I’d recommend practicing problem-solving under time constraints and being prepared to justify your choice of algorithms.
Company Name: American Express
Position: Data Scientist
Application Process: Applied through the company’s career portal.
Interview Rounds:
- Round 1 - Technical Interview:
- Questions Asked:
- Explain XGBoost and its advantages over other algorithms.
- Discuss NLP concepts and their applications.
- Explain linear regression assumptions and how to validate them.
- Your Approach:
- I focused on providing clear, concise explanations and used examples to illustrate my points. I also highlighted my hands-on experience with these models from my projects.
- Outcome: Passed this round. The interviewer appreciated my depth of understanding.
- Questions Asked:
Preparation Tips:
- Be thorough with popular machine learning models like XGBoost, Random Forest, and NLP techniques.
- Ensure you can explain every project or concept mentioned on your resume in detail.
- Practice explaining technical concepts in simple terms, as interviewers often look for clarity.
Conclusion:
The interview was a great learning experience. The interviewer was friendly and made the process comfortable. I realized the importance of being well-prepared with both theoretical and practical aspects of machine learning. For future candidates, I’d advise focusing on clarity and confidence while discussing technical topics.
Company Name: American Express
Position: Data Scientist
Location: [Not specified]
Application Process: Applied through an online application process.
Interview Rounds:
- Round 1 - Phone Interview:
- Questions Asked:
- Recruiter’s self-introduction.
- Whether I fit the position.
- My self-introduction.
- Discussion about job details and responsibilities.
- Salary expectations.
- Willingness to relocate.
- Any specific requirements I had.
- Your Approach:
- Prepared a concise self-introduction highlighting relevant skills and experience.
- Researched the role and company to align my answers with their expectations.
- Was honest about salary expectations and relocation flexibility.
- Outcome:
- The round was more of a screening call to gauge fit and logistics. No technical questions were asked.
- Questions Asked:
Preparation Tips:
- Research the company and role thoroughly to answer fit-related questions confidently.
- Prepare a clear and concise self-introduction.
- Be honest about logistical aspects like salary and relocation.
Conclusion:
The phone interview was straightforward and focused on assessing fit and logistics. It was a good opportunity to clarify role details and expectations. For future candidates, I’d recommend being prepared to discuss these aspects clearly and confidently.
Company Name: American Express
Position: Data Scientist
Location: [Location not specified]
Application Process: The application process was smooth, and the interviewer was very approachable. I went through about four rounds of interviews, including a phone screen.
Interview Rounds:
-
Round 1 - Phone Screen:
- Questions Asked: Basic introductory questions about my background and interest in the role.
- Your Approach: I kept my answers concise and focused on my relevant experience and enthusiasm for the position.
- Outcome: Passed to the next round.
-
Round 2 - Technical Interview (SQL):
- Questions Asked: Questions testing my SQL knowledge, such as writing queries and optimizing them.
- Your Approach: I practiced SQL problems beforehand and made sure to explain my thought process while solving the questions.
- Outcome: Successfully cleared this round.
-
Round 3 - Domain Knowledge Interview:
- Questions Asked: Questions about the credit card industry, such as trends, challenges, and how data science can add value.
- Your Approach: I brushed up on industry-specific knowledge and linked it to my past projects to showcase my understanding.
- Outcome: Advanced to the next stage.
-
Round 4 - Final Interview:
- Questions Asked: A mix of technical and behavioral questions, including scenario-based problems.
- Your Approach: I balanced technical accuracy with clear communication and stayed calm under pressure.
- Outcome: Received positive feedback and moved forward in the process.
Preparation Tips:
- Practice SQL queries thoroughly, especially optimization techniques.
- Research the credit card industry to understand key trends and challenges.
- Prepare for behavioral questions by reflecting on past experiences.
Conclusion:
Overall, the interview process was well-structured and the interviewers were supportive. I felt prepared, but I could have spent more time on industry-specific case studies. My advice to future candidates is to focus on both technical skills and domain knowledge to stand out.
Company Name: American Express
Position: Data Scientist
Location: [Not specified]
Application Process: Applied through university campus placement.
Interview Rounds:
-
Round 1 - Coding Interview (On-campus):
- Questions Asked: Coding problems related to data structures and algorithms.
- Your Approach: Solved the problems using optimal algorithms and explained my thought process clearly.
- Outcome: Passed this round and was invited for an on-site interview.
-
Round 2 - On-site Interview:
- Questions Asked: Technical questions related to data science, including problem-solving and scenario-based questions.
- Your Approach: Demonstrated my analytical skills and provided structured solutions. Also discussed my past projects relevant to the role.
- Outcome: Received a positive response and was offered the position within a few days.
Preparation Tips:
- Focus on coding practice, especially data structures and algorithms.
- Be prepared to discuss your projects and how they align with the role.
- Practice explaining your thought process clearly during problem-solving.
Conclusion:
The interview process was smooth and well-structured. I felt confident because I had prepared thoroughly. My advice to future candidates is to practice coding regularly and be ready to articulate your ideas clearly during interviews.
Company Name: American Express
Position: Data Scientist
Application Process: Applied through the company’s career portal. The process was straightforward, starting with an initial recruiter screening, followed by interviews with the team leader and a live coding session with teammates.
Interview Rounds:
-
Round 1 - Recruiter Screening:
- Questions Asked: Standard questions about strengths and weaknesses.
- Your Approach: Prepared by reflecting on my key strengths and areas for improvement, ensuring my answers aligned with the role’s requirements.
- Outcome: Passed this round and moved to the next stage.
-
Round 2 - Team Leader Interview:
- Questions Asked: Discussed my experience, projects, and how I approach problem-solving.
- Your Approach: Highlighted relevant projects and explained my thought process clearly.
- Outcome: Successfully advanced to the final round.
-
Round 3 - Live Coding Session:
- Questions Asked: Relatively easy coding problems, focusing on data manipulation and basic algorithms.
- Your Approach: Stayed calm, communicated my thought process, and wrote clean, efficient code.
- Outcome: Performed well and received positive feedback.
Preparation Tips:
- Brush up on standard interview questions (strengths, weaknesses, etc.).
- Practice live coding sessions to get comfortable with real-time problem-solving.
- Review basic data manipulation and algorithms.
Conclusion:
Overall, the interview process was smooth and well-structured. The live coding session was easier than expected, but practicing beforehand definitely helped. For future candidates, I’d recommend focusing on clear communication and staying calm during the coding round.
Company Name: American Express
Position: Data Scientist
Location: [Location (if applicable)]
Application Process: [Brief description of how the student applied]
Interview Rounds:
-
Round 1 - Project Discussion:
-
Questions Asked: Detailed discussion about the candidate’s past projects, focusing on methodologies, challenges faced, and outcomes. Some boilerplate data science questions were also included, with a few difficult questions specific to the project for which the hiring was being done.
-
Your Approach: The candidate explained their projects thoroughly, emphasizing their role, the tools and techniques used, and the impact of their work. For the difficult project-specific questions, they tried to relate their past experiences to the new context and admitted when they were unsure, showing a willingness to learn.
-
Outcome: [Result of this round]
-
Round 2 - Technical Interview:
-
Questions Asked: More in-depth technical questions, possibly including coding challenges, statistical concepts, or machine learning algorithms relevant to the role.
-
Your Approach: The candidate prepared by reviewing core data science concepts and practicing coding problems. During the interview, they took their time to think through the problems and communicated their thought process clearly.
-
Outcome: [Result of this round]
(Continue this format for all interview rounds)
Preparation Tips:
-
Review your past projects in detail, as they are likely to be discussed extensively.
-
Brush up on fundamental data science concepts, including statistics, machine learning, and coding.
-
Practice explaining your thought process clearly, especially for difficult or unfamiliar questions.
Conclusion:
The interview process was challenging but rewarding. The candidate felt well-prepared for the project discussion but realized the importance of being adaptable when faced with unfamiliar questions. For future candidates, they recommend thorough preparation on both technical and project-related aspects, as well as staying calm and confident during the interview.