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I am a 4th-year student at IIT Roorkee, and I recently secured a Data Science role at American Express through the on-campus placement process. Here’s a detailed overview ...
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I am a 4th-year student at IIT Roorkee , and I recently secured a Data Science role at American Express through the on-campus placement process. Here’s a detailed overview of the placement process, including my test and interview experiences. About the Role Positions Offered: Software, Data Science, and Product Development roles (focused on Data Science). Eligibility: Open to all branches, with no CGPA criteria. CTC Offered: UG: ₹23.4 LPA PG: ₹24 LPA Placement Process Step 1: Resume-Based Shortlisting Although resume shortlisting was part of the process, all applicants were allowed to proceed to the online test. Step 2: Online Test Platform: Unstop Sections (MCQ format): Numerical Ability: 24 questions Logical Reasoning/Data Interpretation: 24 questions
Coding 12 questions Difficulty Level: Easy to Medium Interestingly, 4–5 questions in my test matched questions from a PDF document I had referenced, shared by students from another IIT. Link: Test Questions Reference Step 3: Interview Process The interview consisted of two rounds, blending technical and HR discussions:
Round 1 Technical + HR Interview Introduction: A brief self-introduction to start the conversation. Resume Discussion: I was asked to describe all my projects. During one project discussion, the interviewer asked about my unique contributions. I admitted that the project was more of a learning experience than a significant contribution. The interviewer appreciated my honesty and moved on. SQL Query: I analyzed two SQL tables and made inferences based on the data provided. Guesstimate Question: I was asked to estimate how many 1 kg packets of Bikaneri Bhujiya are sold in a year. Company Knowledge: The interviewer quizzed me about my understanding of American Express.
Round 2 Technical + HR Interview Two Important Things: The interviewer asked me to share two key points from my resume or life. I mentioned a project and an internship.
Follow-up: I explained that these experiences involved solving real-world problems, and I had included them because they were meaningful to me. Guesstimate Question: The task was to estimate the weight of the International Space Station. Although I presented a detailed breakdown, the interviewer stopped me midway—possibly satisfied with my problem-solving approach or deeming it unnecessary to continue. Puzzle Question: The puzzle involved 10 boxes of balls, where one box contained balls weighing 20 gm each, and the rest had balls weighing 10 gm each. The task was to determine the box with 20 gm balls using the minimum number of weighings. While I couldn’t solve it exactly, I presented three different approaches, which the interviewer appreciated. Key Takeaways Honesty Matters: Acknowledging gaps in your contributions can leave a positive impression if communicated sincerely. Structured Problem-Solving: Even if you don’t arrive at the correct solution, demonstrating your thought process can work in your favor. Preparation is Key: Referencing shared resources like the test question document helped me tackle similar problems during the test. Final Thoughts Securing a Data Science role at American Express was a significant milestone in my academic journey. The process was rigorous but rewarding, testing both my technical and analytical abilities. This experience reinforced the importance of preparation, adaptability, and clear communication in achieving success.
About This Question
This is a candidate experience report from a american express interview for a data science role (intern level) during the oa round reported in 2025.
It covers the following topics: Sql, Networking .
Difficulty rating: Easy
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About American Express Interview Reports
This question was reported by a candidate who interviewed at American Express. LeakCode aggregates interview reports from 10+ sources, including 1Point3Acres, Glassdoor, LeetCode Discuss, Blind, Reddit, Indeed, and Nowcoder. Each report is translated where necessary, deduplicated against existing entries, and tagged by company, role, round type, and reporting date.
Use this question as one calibration data point, not a memorization target. Companies typically rotate their question pools every 2-4 months; the exact wording of a 2024 question may differ from what you encounter today. The underlying pattern, difficulty level, and follow-up depth at American Express are the higher-signal extractions to take from this report.
For broader preparation context, the American Express interview process typically includes a recruiter screen, one or two technical phone screens, and a 4-5 round on-site loop covering coding, system design (at L4+ levels), and behavioral. Reports tagged on LeakCode show the round-by-round distribution and typical difficulty calibration. To browse questions filtered by round type and seniority, use the company hub linked above.
How To Practice This Type of Question
Solve similar problems on LeetCode under timed conditions (25-35 minutes per medium difficulty). The goal is pattern recognition: recognize the underlying technique (sliding window, two-pointer, BFS, memoized recursion, etc.) within 60-90 seconds of reading. Strong candidates verbalize their hypothesis out loud before coding, then iterate based on feedback. Weak candidates dive into implementation immediately, lose time on the wrong approach, and run out of time for follow-ups.
Companies update their question pools every 2-4 months. The exact wording of any given question may have been retired by the time you interview. Focus your prep on the pattern, not the specific problem. The patterns that appear in American Express reports consistently are the ones worth investing in; one-off niche problems are not.
During Your American Express Round
Apply the standard interview round template: clarify requirements (2-3 minutes), state your approach out loud and confirm direction with the interviewer (3-5 minutes), code with narration (15-25 minutes), test with concrete examples including edge cases (5 minutes), discuss optimization or trade-offs if time permits (5 minutes). This template is universally accepted across FAANG and adjacent companies; deviating from it produces weaker interviewer feedback signal.
The single most predictive failure mode in American Express reports tagged "no hire": not asking clarifying questions. Interviewers are explicitly trained to weight this. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into code immediately. The clarifying-question check is often the first signal recorded in the interviewer's written notes.