Interview questions for American Express Management Trainee
Hi everyone, this topic is for sharing Preparation guidelines and interview experience for American Express Management Trainee
The Management Trainee at American Express involves a multi-stage assessment and interview process, designed to evaluate both technical skills and business proficiency. Below is a summary of the process and key points from the interviews you provided:
Assessment Test Rounds:
Resume Shortlist (observed at IIT Chennai, 2016):
Shortlist based on CGPA; resume still weighed for interview shortlist.
Tips: Highlight machine learning/data analytics relevant to the role.
Aptitude/Test:
DSE, 2022: Reasoning, English, Quantitative Aptitude, and Data Interpretation. Emphasis on time management and accuracy.
IIT Chennai, 2016: 45 minutes, 20 questions; basic Quant, Probability, Logical Reasoning, Vocabulary. Tip: Aim to solve all due to ease level.
Interview Rounds:
Technical Interview:
CV/project deep dive (Excel-based analysis), econometrics/statistics, probability/quant, basic ML, and logical/puzzle questions (e.g., GeeksforGeeks).
One-on-one Business/Domain Round:
American Express business model (closed-loop), credit limit setting and optimization, credit card economics.
HR Interview (observed at IIT Chennai, 2016):
Resume walkthrough, motivation for the field/company, and personal background.
Explain your Excel-based research project end-to-end.
How did you clean the data and prepare it for analysis?
What are the Classical Linear Regression Model (CLRM) assumptions?
What is multicollinearity? How would you detect it? Did you find it in your data?
What is the difference between standard error and standard deviation?
Which machine learning models are you familiar with?
Solve a probability problem and explain your reasoning.
Solve a quantitative aptitude problem and walk through your approach.
Solve logical/puzzle questions (e.g., standard GeeksforGeeks puzzles).
What factors would you consider when setting a customer’s credit limit? Formulate an optimization equation reflecting risk-reward trade-offs.
What is the closed-loop model of American Express? Explain how it works and its advantages.
HR/Personality/Behavioral
Introduce yourself.
Walk me through your resume.
Why American Express?
Why do you want to join this field?
Tell us about your family background.
Puzzles/Logical Reasoning
Answer a set of common brain teasers/GeeksforGeeks-style puzzles.
Aptitude/Test Topics (from assessments)
Reasoning
English/Verbal ability/Vocabulary
Quantitative Aptitude
Data Interpretation
Interview Preparation Tips:
Be thorough with econometrics/statistics fundamentals (CLRM assumptions, multicollinearity, SE vs SD).
Study American Express’s business, especially the closed-loop model and basics of credit card economics.
Prepare a clear, structured explanation of projects listed on your CV; clarity is valued over coding depth for this role.
For tests, practice time management and accuracy; expect basic-level Quant, Probability, LR, Vocabulary, and DI.
Highlight ML/data analytics on your resume to strengthen shortlisting chances.
In interviews, articulate your thought process step-by-step for quant/probability/puzzle questions.
Keep HR answers concise and aligned with the role and company values.
If you have attended the process from your campus, pls share your experiences here; Please follow [guidelines](https://discuss.boardinfinity.com/t/interview-transcript-guidelines/22428?u=abhay-gupta-ebaf4123)
Application Process: Applied via campus placement in November 2022.
Interview Rounds:
Round 1 - Aptitude Test:
Questions Asked: Reasoning, English, Quantitative Aptitude, and Data Interpretation.
Your Approach: Focused on time management and accuracy, especially in the Data Interpretation section.
Outcome: Cleared the round successfully.
Round 2 - Technical Round:
Questions Asked:
Introduce yourself.
Explain a project (Excel-based research project on my CV).
How did you clean the data and prepare it for analysis?
CLRM assumptions.
Define multicollinearity. Did you find it in your data?
Difference between standard error and standard deviation.
A few GeeksforGeeks puzzles.
Your Approach: Prepared thoroughly on econometrics and statistics, and practiced explaining my project clearly.
Outcome: Advanced to the next round.
Round 3 - One-on-one Round:
Questions Asked:
Why American Express?
What is the closed-loop model of American Express?
What are the factors you consider for setting the credit limit of a customer? Provide an optimization equation.
Your Approach: Researched American Express’s business model and credit card economics beforehand.
Outcome: Successfully cleared the round.
Preparation Tips:
Focus on credit card economics, econometrics, and statistics.
Read about the business model of American Express, especially the closed-loop model.
Be thorough with the projects mentioned in your CV. Coding knowledge is not compulsory, but clarity on your work is essential.
Conclusion:
The interview process was smooth, and the questions were aligned with the role’s requirements. Preparing well about the company’s business model and brushing up on econometrics helped a lot. For future candidates, I’d advise researching the company thoroughly and being confident while explaining your projects.
Location: Indian Institute of Technology (IIT), Chennai
Application Process: Applied via campus placement at IIT Chennai in December 2016.
Interview Rounds:
Round 1 - Technical Interview:
Questions Asked:
Two puzzles were given.
Your Approach: Solved both puzzles confidently.
Outcome: Passed the round.
Round 2 - HR Interview:
Questions Asked:
General questions about life and family.
Your Approach: Spoke freely and maintained a natural conversation.
Outcome: Successfully cleared the round.
Preparation Tips:
For the Technical Round: Focus on solving puzzles. Practice a variety of puzzles to build confidence.
For the HR Round: Be yourself and maintain a conversational tone. Avoid overthinking the answers.
Conclusion:
The overall interview experience was smooth and engaging. The technical round was straightforward if you’re prepared with puzzles, and the HR round was more about being genuine and articulate. My advice for future candidates is to stay calm and approach each round with confidence.
Company Name: American Express Position: Management Trainee Location: Not specified Application Process: Interviewed in August 2016, likely through campus placement (IIT Kharagpur).
Interview Rounds:
Round 1 - Technical + HR Interview:
Questions Asked:
Walk me through your resume.
Why AMEX and why not higher studies?
If you are a VC, what companies would you invest in?
Tell me about yourself.
Why AMEX? Why job? Why not MBA?
Explain the classification algorithms you used in your project.
Why does a specific algorithm work/doesn’t work for your problem?
What is your weakness?
What do you do in your free time?
What is your favorite Bollywood song and why?
Your Approach: The interviewer focused heavily on my resume, especially my summer project. I ensured I could explain every detail mentioned in my resume, including minor roles or projects.
Outcome: Advanced to the next round.
Round 2 - Case Study Interview:
Questions Asked:
What would happen to AMEX’s income if petrol and diesel prices decrease?
What data would you collect from customers to improve the sales of your supermarket?
Your Approach: The round was interactive, and the interviewer provided hints. I focused on listening carefully and aligning my responses with their thought process.
Outcome: Successfully navigated the case study.
Round 3 - Technical + HR Interview:
Questions Asked: Similar to the first round, with additional pressure-based questions.
Your Approach: Stayed calm under pressure, answered questions directly, and avoided overcomplicating responses.
Outcome: Cleared the round.
Preparation Tips:
Resume Knowledge: Be thorough with every detail on your resume, even minor roles or projects.
Case Study: Practice speaking your thoughts aloud and listening to hints from the interviewer. Follow their direction.
HR Round: Stay composed under pressure and answer questions succinctly.
Conclusion:
The interview process was comprehensive, testing both technical and soft skills. The key takeaway was the importance of being prepared for every aspect of my resume and staying adaptable during case studies. For future candidates, focus on clarity, confidence, and aligning with the interviewer’s expectations.
Application Process: Applied through campus placement at IIT Kharagpur.
Interview Rounds:
Round 1 - Resume Shortlist:
Questions Asked: Resume fitting for a data analysis profile were shortlisted. Mentioning R, Python, and programming skills in the CV was crucial.
Your Approach: Ensured my resume highlighted courses and projects related to data analysis.
Outcome: Shortlisted among 200 students for the next round.
Round 2 - Aptitude Test:
Questions Asked: Quantitative aptitude and logical reasoning questions, such as “Find the least number to be multiplied by 100! such that it’s divisible by 3^50?” and “Find the probability of dice rolled three times to sum up as 10?”.
Your Approach: Prepared using CAT-level aptitude materials. Ensured accuracy as time was ample.
Outcome: Cleared the test successfully.
Round 3 - Technical Interview:
Questions Asked: Focused on motivation (“Why join Amex being from a Mechanical background?”), passion for data analysis, and projects. Ended with a puzzle: “There is a weight balance, and I want to measure from 1 kg to 100 kg. What is the minimum number of stones and their weights required?” Later, the problem was modified to allow weights on both sides.
Your Approach: Discussed my Coursera courses, MTP project on optimization, and genuine interest in data analysis. Solved the initial puzzle but struggled with the modified version.
Outcome: Selected without an HR round. The interviewer appreciated my enthusiasm and problem-solving approach.
Preparation Tips:
Highlight data analysis-related skills and projects in your resume.
Prepare for aptitude tests using CAT-level materials.
Be genuine about your interest in the domain and the company.
Practice puzzles and logical problems to improve problem-solving speed.
Conclusion:
The interview process was more about assessing my passion and fit for the role rather than deep technical knowledge. Being confident and genuine helped me stand out. If I could do something differently, I would practice more puzzles to handle unexpected twists better. For future candidates, focus on showcasing your enthusiasm and adaptability—it goes a long way!
Application Process: [Application process details not provided]
Interview Rounds:
Round 1 - Resume Shortlisting:
Questions Asked: N/A (Resume-based shortlisting)
Your Approach: Tuned my profile to highlight Finance and Analytical skills, including CGPA, Math grades, Olympiads, Economics/OR grades, and leadership roles.
Outcome: Shortlisted for the next round.
Round 2 - Personal Interview:
Questions Asked:
Tell an instance where you were doing things you didn’t like and your reaction.
What does American Express do? What is the AmEx Credit Card Model?
How will you stay motivated through dull phases of your career?
Your Approach: Spoke about a takeover deal falling through as an example. Focused on being straightforward, clear, and showcasing uniqueness in my answers.
Outcome: [Outcome not specified]
Round 3 - Aptitude Test:
Questions Asked: Graded questions with +1 and -1 scoring.
Your Approach: Prepared using a book of 400 puzzles.
Outcome: Cutoff was 8/20, with the maximum score being 14/20.
Preparation Tips:
Tune your resume for Finance and Analytical roles, highlighting CGPA, Math grades, and relevant coursework.
Use resources like puzzle books for aptitude test preparation.
Be confident, specific, and clear in interviews. Effective communication is crucial.
American Express prefers candidates with a minor in Operations Research and strong academic performance.
Conclusion:
The interview process was rigorous but rewarding. Being well-prepared with a tailored resume and practicing puzzles helped me navigate the aptitude test. During the interview, clarity and confidence were key. For future candidates, focus on showcasing your analytical skills and maintaining a strong academic profile.
Location: Indian Institute of Technology (IIT), Chennai
Application Process: Applied via campus placement at IIT Chennai in December 2016.
Interview Rounds:
Round 1 - Resume Shortlist:
Experience: Shortlisting for the test was based on CGPA criteria. However, even if you perform well in the test, they still consider your resume for the interview shortlist.
Tips: Including points relevant to the profile, especially machine learning or data analytics, is a big plus.
Outcome: Shortlisted for the test.
Round 2 - Test:
Experience: The test included very basic-level questions on Quant, Probability, Logical Reasoning, and Vocabulary.
Tips: Since the questions were basic, solving all of them was crucial. The duration was 45 minutes for 20 questions.
Outcome: Cleared the test.
Round 3 - Technical Interview:
Questions Asked:
Machine learning models known.
Probability question.
Quant question.
Your Approach: Prepared thoroughly for machine learning concepts and brushed up on probability and quantitative skills.
Outcome: Advanced to the next round.
Round 4 - HR Interview:
Questions Asked:
Walk me through your resume.
Why do you want to join this field?
Family background.
Your Approach: Answered confidently, focusing on my passion for the field and aligning my skills with the role.
Outcome: Cleared the HR round.
Preparation Tips:
Focus on basic-level Quant, Probability, and Logical Reasoning for the test.
Highlight machine learning or data analytics skills on your resume.
Prepare to discuss your resume in detail during the HR round.
Conclusion:
The overall experience was smooth, and the interviewers were supportive. I could have prepared more thoroughly for the technical questions, but my resume and test performance helped me advance. For future candidates, ensure your resume is tailored to the role, and don’t underestimate the basics in the test!
Location: Indian Institute of Technology (IIT), Chennai
Application Process: Applied via campus placement at IIT Chennai before December 2015.
Interview Rounds:
Round 1 - Test:
Questions Asked: Maths questions, puzzles, aptitude, and verbal ability.
Your Approach: Prepared by practicing aptitude and puzzle-solving techniques.
Outcome: Cleared the round successfully.
Round 2 - Technical + HR Interview:
Questions Asked: Mostly technical questions; case studies and puzzles were also included.
Your Approach: Focused on explaining technical concepts clearly and solving case studies methodically.
Outcome: Performed well and advanced to the next round.
Preparation Tips:
Practice aptitude and puzzle-solving regularly.
Brush up on technical concepts relevant to the role.
Work on case studies to improve problem-solving skills.
Conclusion:
The interview process was thorough but manageable with proper preparation. The technical round was challenging, but practicing case studies and puzzles beforehand helped a lot. For future candidates, I’d recommend focusing on both technical and problem-solving skills to ace the interview.
Application Process: Applied through campus placement at IIT Madras.
Interview Rounds:
Round 1 - Aptitude Test:
Questions Asked: 20 multiple-choice questions covering various topics.
Your Approach: Focused on solving as many questions as possible within the 1-hour timeframe.
Outcome: Needed to solve 13-14 questions to pass; the test was challenging.
Round 2 - Technical Interview:
Questions Asked: Topics included multivariate data analysis, probability and statistics, linear algebra, machine learning, algorithms, Python, and R.
Your Approach: Prepared thoroughly on the mentioned topics and practiced problem-solving.
Outcome: Advanced to the next round.
Round 3 - Technical Interview:
Questions Asked: Similar to the first technical round, focusing on data analysis and programming skills.
Your Approach: Revisited key concepts and practiced coding problems.
Outcome: Successfully cleared the round.
Round 4 - HR Interview:
Questions Asked: General HR questions about background, motivation, and fit for the role.
Your Approach: Prepared answers for common HR questions and researched the company.
Outcome: Progressed to the final HR round.
Round 5 - HR Interview:
Questions Asked: Further HR questions, including behavioral and situational queries.
Your Approach: Maintained confidence, answered honestly, and aligned responses with the company’s values.
Outcome: Cleared the round and received positive feedback.
Preparation Tips:
Technical Preparation: Focus on multivariate data analysis, probability and statistics, linear algebra, machine learning, algorithms, Python, and R.
HR Preparation: Practice general HR questions, craft a strong resume, and research the company thoroughly.
Aptitude Test: Solve practice problems to improve speed and accuracy.
Conclusion:
The interview process was rigorous but well-structured. The technical rounds tested deep knowledge of data analysis and programming, while the HR rounds assessed fit and motivation. Preparing thoroughly for both technical and HR aspects was key to success. For future candidates, I recommend dedicating ample time to study the mentioned topics and practicing HR questions to build confidence.
Application Process: Applied through campus placement at IIT Madras.
Interview Rounds:
Round 1 - Aptitude Test:
Questions Asked: 20 questions covering various topics.
Your Approach: Focused on time management and accuracy.
Outcome: Found the test very difficult but managed to pass.
Round 2 - Technical Interview:
Questions Asked: Data analysis questions, detailed questions about internships and projects mentioned in the resume.
Your Approach: Explained projects clearly, emphasized hands-on experience with tools like Python and R.
Outcome: Advanced to the next round.
Round 3 - Technical Interview:
Questions Asked: Similar to Round 2, with deeper dives into data analysis techniques and algorithms.
Your Approach: Used examples from coursework and internships to demonstrate skills.
Outcome: Successfully cleared the round.
Round 4 - HR Interview:
Questions Asked: Personal interview focusing on fit for the role and company culture.
Your Approach: Researched the company thoroughly and aligned answers with their values.
Outcome: Positive feedback and final selection.
Preparation Tips:
Brush up on multivariate data analysis, probability, statistics, linear algebra, machine learning, and algorithms.
Practice coding in Python and R.
Prepare for HR questions by researching the company and aligning your answers with their culture.
Revise your resume thoroughly, especially internships and projects.
Conclusion:
The interview process was rigorous but rewarding. The aptitude test was particularly challenging, so time management is key. Technical rounds focused heavily on practical knowledge, so be prepared to discuss your projects in detail. Researching the company beforehand helped a lot in the HR round. Overall, a great learning experience!
Application Process: Applied through campus placement.
Interview Rounds:
Round 1 - Aptitude Test:
Questions Asked: 20 MCQs with a +1/-1 marking scheme. Probability was a major topic.
Your Approach: Focused on solving probability questions and ensured accuracy due to the negative marking.
Outcome: Cleared the round successfully.
Round 2 - Interview:
Questions Asked: Questions revolved around analytical skills, math skills, and familiarity with programming in R and Python. Prior experience in machine learning was also discussed.
Your Approach: Highlighted my comfort with programming languages like R and Python and showcased my prior experience in machine learning.
Outcome: Successfully cleared the interview round.
Preparation Tips:
Focus on analytical and math skills, especially probability.
Ensure you are comfortable with programming in R and Python, as it adds value to your profile.
Prior experience in machine learning is a big plus, so highlight it if you have it.
Conclusion:
The interview process was smooth, and the focus was primarily on analytical and math skills rather than PoRs. Being comfortable with programming languages like R and Python and having prior experience in machine learning worked in my favor. For future candidates, I’d recommend brushing up on probability and ensuring your resume reflects your technical skills clearly.
Application Process: Applied through campus placement at IIT Kharagpur. The initial shortlist of around 30 students was done based on CV submission.
Interview Rounds:
Round 1 - Curriculum Vitae Submission:
Questions Asked: None explicitly, but CV was evaluated for academic credentials and math/statistics-based work.
Your Approach: Ensured my CV highlighted academic achievements and any relevant projects or internships.
Outcome: Shortlisted for the next round.
Round 2 - Test:
Questions Asked:
A question based on conditional probability.
An intuitive question about credit card division (e.g., “What is a score card?”).
Your Approach: Quantified results clearly and stated assumptions explicitly. Asked for clarifications where needed.
Outcome: Advanced to the next round.
Round 3 - Technical Interview:
Questions Asked:
“Why American Express?”
Discussion about projects and internships.
Why I opted for FRM and why I didn’t pursue higher studies.
A case study on credit card division.
A puzzle.
Your Approach: Prepared thoroughly for common questions like “Why American Express?” and discussed my projects confidently. For the case study, I used logical reasoning and communicated clearly.
Outcome: Successfully cleared the round.
Preparation Tips:
Highlight academic credentials and math/statistics-based work in your CV.
Practice puzzles and case study questions.
Have a deep understanding of your projects, especially if they involve statistics, quant, or pattern recognition.
Communication skills are crucial—be clear and confident.
Research the company thoroughly to answer “Why this company?” convincingly.
During case interviews, listen to hints from the interviewer and use them to guide your answers.
Conclusion:
The interview process was friendly and engaging. The interviewers were knowledgeable and gave helpful hints during the case study. I realized the importance of being confident about my resume and using the interviewer’s cues effectively. For future candidates, I’d advise thorough preparation, especially in probability and case studies, and to stay calm and composed during the interview.
Application Process: Applied through campus placement at IIT Madras.
Interview Rounds:
Round 1 - Aptitude Test:
Questions Asked: 20 questions with negative marking. The test was relatively harder compared to other analytics companies.
Your Approach: Focused on accuracy due to negative marking and practiced similar aptitude questions beforehand.
Outcome: Cleared the round successfully.
Round 2 - Technical Interview (Part 1):
Questions Asked:
How do they use data analytics in their field of work?
Tell about any analytics projects you’ve done and general programming skills.
How to use data analytics for credit cards.
Your Approach: Explained real-world applications of data analytics in financial services and discussed personal projects involving data analysis and programming.
Outcome: Advanced to the next technical round.
Round 3 - Technical Interview (Part 2):
Questions Asked: Similar to the first technical round but deeper dive into technical skills and problem-solving.
Your Approach: Demonstrated problem-solving abilities and linked answers to practical scenarios in credit card analytics.
Outcome: Cleared the round.
Round 4 - HR Interview (Part 1):
Questions Asked: General HR questions about background, motivation, and fit for the role.
Your Approach: Stayed honest and aligned answers with the company’s values and role requirements.
Outcome: Cleared the round.
Round 5 - HR Interview (Part 2):
Questions Asked: Further discussions on cultural fit, long-term goals, and situational questions.
Your Approach: Emphasized adaptability and enthusiasm for the role.
Outcome: Received positive feedback and moved forward in the process.
Preparation Tips:
Focus on aptitude practice with timed tests to handle negative marking.
Brush up on data analytics concepts, especially in financial services.
Prepare to discuss past projects in detail, highlighting technical skills.
Practice HR questions to articulate motivations and fit clearly.
Conclusion:
The interview process was thorough but well-structured. The technical rounds required a strong grasp of data analytics and its applications in finance, while the HR rounds were more about cultural fit. Practicing aptitude tests and being clear about my projects helped a lot. For future candidates, I’d recommend focusing on both technical depth and soft skills to ace all rounds.
Application Process: The application process details were not provided.
Interview Rounds:
Round 1 - Aptitude Test:
Questions Asked:
The test included 20 questions with negative marking. Topics covered mathematics and logical puzzles, with a focus on permutation and combination.
Your Approach:
Focused on accuracy due to negative marking. Prioritized solving permutation and combination problems first.
Outcome:
Cleared the round successfully.
Round 2 - Technical Interview (Case Study):
Questions Asked:
Case studies related to the credit card business were given. For example:
“A person is described. He comes and asks for a credit card. How do you decide to give him a credit card?”
“A cricket match is going on at Eden Gardens. Estimate the number of 10 rupee notes in the entire stadium.”
Your Approach:
Analyzed the case studies logically and provided structured answers. For the guesstimate question, broke down the problem into smaller parts (e.g., stadium capacity, average cash carried by attendees).
Outcome:
Advanced to the next round.
Round 3 - Technical Interview (Follow-up):
Questions Asked:
Similar to the first technical round, with additional case studies and guesstimate questions.
Your Approach:
Applied the same structured approach, ensuring clarity and logical reasoning in responses.
Outcome:
Cleared the round.
Round 4 - HR Interview:
Questions Asked:
General HR questions about background, motivation, and fit for the role.
Your Approach:
Answered honestly and aligned responses with the company’s values and the role’s requirements.
Outcome:
Successfully cleared the round.
Round 5 - Final Interview:
Questions Asked:
A mix of technical and behavioral questions to assess overall suitability.
Your Approach:
Balanced technical knowledge with soft skills, demonstrating problem-solving abilities and cultural fit.
Outcome:
Received positive feedback and moved forward in the process.
Preparation Tips:
Focus on permutation and combination for the aptitude test.
Practice case studies and guesstimate questions, especially those related to the credit card industry.
Improve communication skills and clarity in expressing thoughts.
Solve previous years’ question papers to get a feel for the test pattern.
Conclusion:
The interview process was thorough and tested both technical and analytical skills. The case studies and guesstimate questions were challenging but manageable with proper preparation. Communication skills played a crucial role in the HR and final rounds. For future candidates, I recommend practicing logical puzzles and case studies extensively and being clear and concise in responses.
Application Process: The application process details were not provided, but it likely involved campus placement or an online application given the context of the interview rounds.
Interview Rounds:
Round 1 - Group Discussion:
Questions Asked: Topics were not specified, but the discussion likely revolved around customer-centric scenarios or general business topics.
Your Approach: Focused on active participation, clear communication, and aligning points with the company’s customer-centric values.
Outcome: Successfully cleared the round.
Round 2 - Personal Interview:
Questions Asked: Personal interview questions were not detailed, but they might have included behavioral and situational questions.
Your Approach: Highlighted relevant experiences and skills, emphasizing problem-solving and teamwork.
Outcome: Advanced to the next round.
Round 3 - Aptitude Test:
Questions Asked: Details about the aptitude test were not provided, but it likely included quantitative, logical, and verbal reasoning sections.
Your Approach: Prepared using standard aptitude resources and practiced time management.
Outcome: Cleared the test successfully.
Round 4 - Technical/Other Interview:
Questions Asked: Specific questions were not mentioned, but the round might have involved scenario-based or technical discussions.
Your Approach: Focused on demonstrating coding skills and problem-solving abilities.
Outcome: Progressed further in the process.
Round 5 - Final Interview:
Questions Asked: No specific details were shared, but it could have been a mix of behavioral, technical, and fit questions.
Your Approach: Reinforced alignment with the company’s values and showcased readiness for the role.
Outcome: Final outcome was not specified.
Preparation Tips:
Familiarize yourself with the company’s customer-centric approach, as it is a key focus area.
Brush up on coding skills and problem-solving techniques.
Practice group discussions and aptitude tests to improve performance.
Have a clear overview of your projects and experiences to discuss them confidently.
Conclusion:
The interview process at American Express was comprehensive, with a strong emphasis on customer-centric values and problem-solving skills. While the experience was positive, more detailed preparation for each round could have further enhanced performance. Future candidates should focus on understanding the company’s core values and practicing all types of interview rounds thoroughly.
Application Process: Applied through campus placement at IIT Madras.
Interview Rounds:
Round 1 - Group Discussion:
Questions Asked: Topics related to customer-centric approaches and e-commerce trends.
Your Approach: Focused on articulating the importance of customer satisfaction in the credit card and net banking space.
Outcome: Cleared the round with positive feedback on communication skills.
Round 2 - Personal Interview:
Questions Asked: Questions about personal projects, understanding of customer service, and problem-solving scenarios.
Your Approach: Highlighted relevant projects and experiences, emphasizing problem-solving and customer-centric thinking.
Outcome: Advanced to the next round.
Round 3 - Aptitude Test:
Questions Asked: Quantitative and logical reasoning questions.
Your Approach: Practiced common aptitude problems beforehand and managed time effectively during the test.
Outcome: Successfully cleared the round.
Round 4 - Technical Interview:
Questions Asked: Questions about coding skills and problem-solving abilities.
Your Approach: Demonstrated coding proficiency and logical thinking through examples from past projects.
Outcome: Moved to the final round.
Round 5 - Final Interview:
Questions Asked: Behavioral and situational questions to assess fit for the Management Trainee role.
Your Approach: Shared experiences that aligned with the company’s values and demonstrated leadership potential.
Outcome: Received an offer for the position.
Preparation Tips:
Have a solid overview of your projects and be ready to discuss them in detail.
Brush up on coding skills if applicable.
Practice aptitude tests and group discussions to improve performance.
Conclusion:
The interview process was thorough and focused on assessing both technical and behavioral competencies. Being well-prepared with project details and practicing problem-solving scenarios helped me succeed. For future candidates, understanding the company’s customer-centric approach and aligning your responses accordingly can make a significant difference.
Application Process: Applied through campus placement at IIT Kharagpur.
Interview Rounds:
Round 1 - Curriculum Vitae Submission:
Experience: Only an initial shortlist of around 30 students was done based on CV.
Tips: Mention your academic credentials, math/statistics-based work done (if any).
Outcome: Shortlisted for the next round.
Round 2 - Test:
Questions Asked:
Subjective question based on probability theory.
Question related to the credit card division (e.g., “What is a score card?”).
Your Approach: Quantified results, stated assumptions clearly, and ensured answers were complete.
Outcome: Shortlisted for the next round.
Round 3 - Technical Interview:
Questions Asked:
“Why American Express?”
Discussion about projects and internship.
Questions about opting for FRM and not pursuing higher studies.
Case study question on the credit card division.
A puzzle.
Your Approach: Practiced puzzles and case studies beforehand. Communicated clearly and confidently about my projects and interests.
Outcome: Successfully cleared the round.
Preparation Tips:
Practice puzzles and case study questions.
Have a good understanding of your projects, especially if they are based on statistics, quant, or pattern recognition.
Improve communication skills.
Research the company thoroughly to align your answers with their profile.
Conclusion:
The interview process was peaceful and friendly. Being confident about my resume and using hints given during the case interview helped a lot. A key takeaway is to align your background with the company’s needs and communicate effectively. Good luck to future candidates!
Questions Asked: 20 questions with negative marking. The test is relatively harder compared to other analytics companies.
Your Approach: Focused on accuracy due to negative marking and practiced similar aptitude questions beforehand.
Outcome: Cleared the round.
Round 2 - Technical Interview (Part 1):
Questions Asked:
How do they use data analytics in their field of work?
Tell about any analytics projects you’ve done and general programming skills.
How to use data analytics for credit cards.
Your Approach: Prepared by reviewing past analytics projects and brushing up on programming concepts. Answered with practical examples.
Outcome: Advanced to the next round.
Round 3 - Technical Interview (Part 2):
Questions Asked: [Details not provided]
Your Approach: [Details not provided]
Outcome: [Details not provided]
Round 4 - HR Interview (Part 1):
Questions Asked: [Details not provided]
Your Approach: Stayed confident and answered questions honestly.
Outcome: Cleared the round.
Round 5 - HR Interview (Part 2):
Questions Asked: [Details not provided]
Your Approach: Maintained a professional and friendly demeanor.
Outcome: [Details not provided]
Preparation Tips:
Practice aptitude tests with negative marking to improve accuracy.
Revise analytics projects and programming skills for technical rounds.
Prepare for HR rounds by practicing common HR questions and staying confident.
Conclusion:
The interview process was thorough, with a mix of technical and HR rounds. The aptitude test was challenging due to negative marking, so accuracy is key. Technical rounds required a good understanding of data analytics and programming. HR rounds were straightforward but required confidence and clarity. Overall, preparing well for each stage helped me navigate the process successfully.
Application Process: The application process details were not provided, but it likely involved campus placement or an online application given the context of the interview rounds.
Interview Rounds:
Round 1 - Aptitude Test:
Questions Asked: The test included 20 questions with a focus on mathematics and logical puzzles. Topics like permutation and combination were prominent.
Your Approach: Since there was negative marking, I was cautious and focused on accuracy rather than attempting all questions. I prioritized solving problems I was confident about.
Outcome: The test was challenging, but I managed to clear it by focusing on strong areas like permutation and combination.
Round 2 - Technical Interview (Case Study):
Questions Asked: Case studies related to the credit card business were given. For example:
“A person comes and asks for a credit card. How do you decide whether to approve or deny the request?”
“Estimate the number of 10-rupee notes in the Eden Gardens stadium during a cricket match.”
Your Approach: I analyzed the case studies logically, considering factors like creditworthiness for the credit card question and breaking down the stadium estimation problem into smaller, manageable parts.
Outcome: The interviewers seemed satisfied with my logical reasoning and problem-solving approach.
Round 3 - Technical Interview (Follow-up):
Questions Asked: Further case studies and guesstimate questions were asked, continuing the theme of credit card business scenarios.
Your Approach: I built on my previous experience, ensuring clarity in communication and justifying my reasoning for each answer.
Outcome: I performed well and advanced to the next round.
Round 4 - HR Interview:
Questions Asked: General HR questions about my background, motivation for joining American Express, and how I handle teamwork and challenges.
Your Approach: I answered honestly, aligning my responses with the company’s values and emphasizing my adaptability and problem-solving skills.
Outcome: The round went smoothly, and I received positive feedback.
Round 5 - Final Interview:
Questions Asked: A mix of technical and behavioral questions to assess overall fit for the role.
Your Approach: I balanced technical knowledge with soft skills, demonstrating my ability to think critically and work collaboratively.
Outcome: I successfully cleared this round and received the offer.
Preparation Tips:
Focus on permutation and combination for the aptitude test.
Practice case studies and guesstimate questions, especially those related to the credit card or financial industry.
Work on communication skills, as clarity and confidence are key during interviews.
Solve previous years’ question papers to get a feel for the test pattern.
Conclusion:
The interview process was rigorous but well-structured. The case studies and guesstimates were challenging but also the most rewarding part. I could have prepared more for the logical puzzles in the aptitude test. My advice to future candidates is to practice extensively, especially for the technical rounds, and to stay calm and composed during the interviews. Good luck!
Location: Indian Institute of Technology (IIT), Chennai
Application Process: Applied via campus placement at IIT Chennai in December 2016.
Interview Rounds:
Round 1 - Resume Shortlist:
Experience: Shortlisting for the test was based on CGPA criteria. However, even if you performed well in the test, the resume was still considered for the interview shortlist.
Tips: Including points relevant to the profile, especially machine learning or data analytics, was a big plus.
Outcome: Shortlisted for the next round.
Round 2 - Test:
Experience: The test included very basic-level questions on Quant, Probability, Logical Reasoning, and Vocabulary.
Tips: Solving all questions was crucial due to their basic nature.
Duration: 45 minutes
Total Questions: 20
Outcome: Cleared the test and moved to the interview rounds.
Round 3 - Technical Interview:
Questions Asked:
Machine learning models known.
Probability question.
Quant question.
Your Approach: Focused on explaining the concepts clearly and logically. For the probability and quant questions, I walked through my thought process step-by-step.
Outcome: Advanced to the next round.
Round 4 - HR Interview:
Questions Asked:
Walk me through your resume.
Why do you want to join this field?
Family background.
Your Approach: I kept my answers concise and aligned them with the role and company values. For the “why this field” question, I highlighted my passion and relevant skills.
Outcome: Successfully cleared the HR round.
Preparation Tips:
Focus on basic quant, probability, and logical reasoning for the test.
Highlight machine learning or data analytics skills in your resume.
Practice explaining your resume and motivations clearly for the HR round.
Conclusion:
Overall, the interview process was smooth and well-structured. The key was to perform well in the test and ensure the resume stood out. For future candidates, I’d recommend brushing up on basic concepts and tailoring the resume to the role.