Company Name: American Express
Position: Data Scientist
Location: [Location not specified]
Application Process: Applied through an online application process.
Interview Rounds:
Conclusion:
The interview process was thorough and professional until the final stages, where I experienced complete silence without any updates or rejection. It was disappointing, especially from a reputed company like American Express. My advice to future candidates is to stay proactive in following up and not to rely solely on the company’s communication timeline.
Company Name: American Express
Position: Data Scientist
Application Process: Applied online for the Data Scientist position.
Interview Rounds:
Conclusion:
The interview process with American Express for the Data Scientist role was highly disappointing. The lack of professionalism, irrelevant questions, and disinterested interviewers made it a waste of time. If you’re applying here, be prepared for a disorganized process and generic questions. It’s unclear whether this reflects the company culture, but it certainly didn’t leave a good impression.
Company Name: American Express
Position: Data Scientist
Location: [Not specified]
Application Process: I was referred to the team’s manager, and the team contacted me directly for the interview process.
Interview Rounds:
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Round 1 - Technical Phone Interview:
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Questions Asked: The interview lasted 1 hour and covered technical topics relevant to the Data Scientist role. Specific questions were not shared.
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Your Approach: I prepared by reviewing key data science concepts and algorithms, as well as my past projects.
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Outcome: I passed this round and was invited for the onsite interview.
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Round 2 - Onsite Interview:
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Questions Asked: This was a half-day interview, likely involving technical and behavioral questions, though specifics were not detailed.
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Your Approach: I focused on demonstrating my problem-solving skills and aligning my experience with the role’s requirements.
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Outcome: I received an offer two days later.
Conclusion:
The interview process itself was smooth, and I was excited to receive the offer. However, the post-offer experience was extremely disorganized. The HR team was slow to respond, made errors in documents, and ultimately withdrew the offer after two months due to an export license issue they failed to address earlier. This caused significant disruptions to my academic and professional plans. My advice to future candidates is to be cautious and ensure all logistical details are clarified early in the process to avoid similar situations.
Company Name: American Express
Position: Data Scientist
Application Process: Applied through the company’s career portal.
Interview Rounds:
Preparation Tips:
- Review your resume thoroughly and be ready to explain any project in detail.
- Brush up on ML concepts and case study frameworks.
- Practice explaining your thought process clearly for case studies.
Conclusion:
The interview process was smooth, and the interviewers were friendly. I felt prepared for the technical aspects but could have practiced more case studies to improve my confidence. For future candidates, focus on clarity and structure in your responses.
Company Name: American Express
Position: Data Scientist
Application Process: The application was submitted online, and the first round was a call with the hiring manager.
Interview Rounds:
- Round 1 - Manager Call:
- Questions Asked: The manager asked questions about machine learning concepts and my experience in the field. The discussion lasted about 20 minutes.
- Your Approach: I focused on explaining my understanding of machine learning models, algorithms, and real-world applications. I also highlighted my past projects and how they align with the role.
- Outcome: Passed this round and was informed about the upcoming rounds, which include a coding test, technical test, HR interview, and an onsite interview.
Preparation Tips:
- Brush up on core machine learning concepts and algorithms.
- Be ready to discuss your projects in detail, focusing on problem-solving and impact.
- Practice coding problems, especially those related to data manipulation and algorithms.
Conclusion:
The first round was a good start, and the manager was very approachable. I feel confident about the upcoming rounds but will focus more on coding practice and technical depth to ensure I perform well.
Company Name: American Express
Position: Data Scientist
Location: [Location not specified]
Application Process: Applied through a recruiter screen followed by a phone interview and an onsite interview. The initial rounds were completed within 2 weeks, but the final decision took an additional 2 months.
Interview Rounds:
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Round 1 - Recruiter Screen:
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Questions Asked: General questions about my background, experience, and interest in the role.
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Your Approach: I kept my answers concise and aligned them with the job description.
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Outcome: Passed to the next round.
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Round 2 - Phone Interview:
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Questions Asked: Technical questions related to data science, including algorithms, data structures, and past projects.
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Your Approach: I focused on explaining my thought process clearly and provided examples from my previous work.
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Outcome: Advanced to the onsite interview.
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Round 3 - Onsite Interview:
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Questions Asked: In-depth technical questions, case studies, and behavioral questions. Topics included machine learning models, data analysis, and problem-solving scenarios.
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Your Approach: I prepared by reviewing key concepts and practicing mock interviews. During the interview, I structured my answers logically and asked clarifying questions when needed.
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Outcome: Successfully cleared the round but had to wait 2 months for the final offer.
Preparation Tips:
- Review core data science concepts, especially algorithms and machine learning models.
- Practice explaining your past projects and how they relate to the role.
- Be prepared for behavioral questions; use the STAR method to structure your answers.
Conclusion:
The interview process was thorough but the long wait for the final decision was frustrating. My advice is to stay patient and continue preparing for other opportunities while waiting for the outcome. The key is to perform well in the interviews and trust the process, even if it takes longer than expected.
Company Name: American Express
Position: Data Scientist
Location: [Not specified]
Application Process: [Not specified]
Interview Rounds:
- Round 1 - Technical Round:
- Questions Asked:
- How do you define model Gini?
- How do you train an XGBoost model?
- Your Approach:
- For the first question, I explained the concept of the Gini coefficient in the context of model evaluation, emphasizing its use in measuring inequality among values of a frequency distribution.
- For the second question, I walked through the steps of training an XGBoost model, including parameter tuning, cross-validation, and feature importance.
- Outcome: [Not specified]
Preparation Tips:
- Focus on understanding key machine learning concepts like model evaluation metrics (e.g., Gini coefficient) and popular algorithms (e.g., XGBoost).
- Practice explaining technical concepts clearly and concisely.
- Review SQL, analytical skills, and big data technologies as they are relevant to the role.
Conclusion:
The interview was a good opportunity to test my technical knowledge, especially around model evaluation and algorithm training. I felt confident in my answers but would recommend practicing more real-world applications of these concepts to further solidify understanding.
Company Name: American Express
Position: Data Scientist
Location: [Location not specified]
Application Process: [Application process details not provided]
Interview Rounds:
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Round 1 - Technical Interview with Senior Team Member:
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Questions Asked:
- Questions related to statistics and machine learning concepts.
- Specific examples of how you applied these concepts in past projects.
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Your Approach:
- Explained theoretical concepts clearly and provided real-world examples from previous work.
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Outcome:
- Successfully passed to the next round.
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Round 2 - Technical Interview with Another Senior Team Member:
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Questions Asked:
- More in-depth questions on machine learning algorithms and their applications.
- Discussion on problem-solving approaches for hypothetical scenarios.
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Your Approach:
- Focused on demonstrating practical knowledge and problem-solving skills.
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Outcome:
- Advanced to the final round.
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Round 3 - Interview with VP:
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Questions Asked:
- High-level questions about data science trends and how they align with business goals.
- Behavioral questions about teamwork and leadership.
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Your Approach:
- Balanced technical insights with business acumen and emphasized collaboration.
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Outcome:
Preparation Tips:
- Brush up on core statistics and machine learning concepts.
- Be ready to discuss past projects in detail.
- Practice explaining technical ideas in simple terms for non-technical stakeholders.
Conclusion:
The interview process was thorough but fair, focusing on both technical expertise and alignment with business objectives. I felt well-prepared for the technical rounds but could have practiced more behavioral questions for the VP round. My advice for future candidates is to balance technical preparation with soft skills and business awareness.
Company Name: American Express
Position: Data Scientist
Location: [Location not specified]
Application Process: [Details not provided]
Interview Rounds:
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Round 1 - Aptitude Test:
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Questions Asked: Multiple-choice questions (MCQs) similar to the CAT exam.
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Your Approach: Prepared by practicing quantitative and logical reasoning questions. Focused on time management to ensure all questions were attempted.
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Outcome: Successfully cleared the round.
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Round 2 - Case Study:
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Questions Asked: Machine Learning case study, e.g., loan default prediction.
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Your Approach: Analyzed the problem, discussed data preprocessing, feature selection, and model choices. Justified the approach with reasoning.
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Outcome: [Outcome not specified]
Preparation Tips:
- Brush up on quantitative aptitude and logical reasoning for the aptitude test.
- Practice ML case studies, especially those related to financial domains like loan prediction.
- Be ready to explain your thought process clearly during the case study round.
Conclusion:
The interview process was structured and tested both analytical and problem-solving skills. The case study round was particularly insightful, and I would advise future candidates to focus on practical ML applications and clear communication of their ideas.
Company Name: American Express
Position: Data Scientist
Application Process: Applied via campus placement before July 2023.
Interview Rounds:
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Round 1 - Aptitude Test:
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Questions Asked: General aptitude questions (medium difficulty) and technical questions covering topics like Big Data and Python.
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Your Approach: Focused on solving the aptitude questions methodically and brushed up on Big Data and Python concepts beforehand.
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Outcome: Cleared the round successfully.
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Round 2 - Technical Interview:
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Questions Asked: Detailed questions on ML algorithms like SVM, Random Forest, Bagging, Boosting, Ridge, etc.
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Your Approach: Explained the theoretical concepts clearly and provided practical examples where applicable.
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Outcome: Advanced to the next round.
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Round 3 - Technical Interview:
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Questions Asked: Deep dive into equations and understanding of DL and ML algorithms.
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Your Approach: Demonstrated a strong grasp of the mathematical foundations behind the algorithms and their applications.
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Outcome: Successfully cleared the round.
Preparation Tips:
- Focus on core ML and DL concepts, including the mathematical underpinnings.
- Practice explaining algorithms clearly with examples.
- Brush up on Python and Big Data technologies.
Conclusion:
The interview process was thorough and tested both theoretical knowledge and practical understanding of ML/DL concepts. Preparing well for the technical rounds and staying calm during the interviews helped me perform better. Future candidates should ensure they have a solid grasp of the fundamentals and can articulate their thoughts clearly.
Company Name: American Express
Position: Data Scientist
Location: [Not specified]
Application Process: Applied via campus placement at Birla Institute of Technology and Science (BITS), Pilani.
Interview Rounds:
- Round 1 - Technical Round:
- Questions Asked:
- Can you describe the work you did in your previous company?
- Your Approach: I focused on highlighting my key contributions, the projects I worked on, and the impact of my work. I also mentioned any specific tools or technologies I used, such as Python, machine learning, and data analytics.
- Outcome: [Result not specified]
Preparation Tips:
- Brush up on your past work experience and be ready to discuss it in detail.
- Familiarize yourself with common data science tools and technologies like Python, machine learning, and data analytics.
- Practice explaining your projects clearly and concisely.
Conclusion:
The interview was straightforward, focusing mainly on my previous work experience. It would have been helpful to prepare more specific examples of my contributions and their impact. For future candidates, make sure you can articulate your past projects and their relevance to the role you’re applying for.
Company Name: American Express
Position: Data Scientist
Location: [Not specified]
Application Process: I applied via a job portal and was interviewed in November 2023.
Interview Rounds:
- Round 1 - One-on-one Round:
- Questions Asked:
- What is Gradient Descent?
- What is LSTM, and what are the gates in it?
- They gave me a link to a dataset and asked me to perform operations like
value_counts, checking null_values, filling missing values with the mean, etc.
- What is a t-test? What are Mean, Median, and Mode, and where are these used?
- What is Random Forest?
- Your Approach:
- For theoretical questions like Gradient Descent, LSTM, and Random Forest, I explained the concepts clearly, providing examples where applicable.
- For the dataset task, I followed the instructions step-by-step, ensuring I understood the operations and their implications.
- For statistical questions like the t-test and measures of central tendency, I linked them to real-world applications to demonstrate understanding.
- Outcome: [Result not specified]
Preparation Tips:
- Focus on core topics like Machine Learning, Statistics, and Pandas.
- Practice hands-on data manipulation tasks, as they might ask you to perform operations on a dataset during the interview.
- Revise fundamental statistical concepts and their practical applications.
Conclusion:
The interview was quite technical, with a strong emphasis on both theoretical knowledge and practical skills. While I felt confident in my explanations, I could have prepared more thoroughly for the dataset task by practicing similar exercises beforehand. For future candidates, I’d recommend balancing theory with hands-on practice to ace such rounds.
Company Name: American Express
Position: Data Scientist
Location: [Not specified]
Application Process: I applied through the company’s official website and was interviewed before February 2023.
Interview Rounds:
Preparation Tips:
- Focus on fundamental machine learning concepts, especially regression and regularization.
- Practice explaining these concepts in simple terms to ensure clarity during the interview.
- Review practical applications and case studies where these techniques are used.
Conclusion:
The interview was a good learning experience, and it highlighted the importance of being thorough with foundational ML topics. For future candidates, I’d recommend dedicating time to understanding the theory behind the concepts and being ready to discuss real-world applications.
Company Name: American Express
Position: Data Scientist
Application Process: I applied via IIM Jobs and was interviewed before June 2023.
Interview Rounds:
- Round 1 - Technical Round:
- Questions Asked:
- SQL basic questions
- Python - pandas, numpy-based questions
- Your Approach: I prepared by revising SQL fundamentals and practicing Python libraries like pandas and numpy. For SQL, I focused on queries, joins, and aggregations. For Python, I brushed up on data manipulation and analysis techniques.
- Outcome: The round went well, and I was able to answer the questions confidently.
Preparation Tips:
- Revise SQL basics, including queries, joins, and aggregations.
- Practice Python libraries like pandas and numpy for data manipulation.
- Focus on real-world applications of these tools in data science.
Conclusion:
Overall, the interview was a great learning experience. American Express is a fantastic place to work, and the technical round was straightforward if you’re well-prepared. My advice to future candidates is to focus on practical applications of SQL and Python, as the questions are more about problem-solving than theoretical knowledge.
Company Name: American Express
Position: Data Scientist
Location: [Not specified]
Application Process: [Not specified]
Interview Rounds:
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Round 1 - Resume Shortlist:
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Questions Asked: Resume review and shortlisting.
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Your Approach: Ensured my resume was crisp and highlighted relevant skills and experiences.
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Outcome: Passed to the next round.
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Round 2 - Coding Test:
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Questions Asked: Basic DP (Dynamic Programming) and array-related questions.
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Your Approach: Focused on solving problems efficiently and optimizing code.
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Outcome: Cleared the coding round.
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Round 3 - One-on-one Technical Round:
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Questions Asked: Resume walkthrough and discussion, followed by medium-level coding questions.
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Your Approach: Explained my projects and experiences clearly, and solved coding problems methodically.
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Outcome: Advanced to the next round.
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Round 4 - One-on-one Manager Round:
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Questions Asked: Discussion with the manager about my fit for the role and team.
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Your Approach: Highlighted my problem-solving skills and alignment with the company’s goals.
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Outcome: Successfully cleared the round.
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Round 5 - HR Round:
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Questions Asked: General HR questions about my background, expectations, and cultural fit.
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Your Approach: Answered honestly and aligned my responses with the company’s values.
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Outcome: Received positive feedback and moved forward in the process.
Preparation Tips:
- Focus on Dynamic Programming and array-related coding problems.
- Be thorough with your resume and ready to discuss any project or experience mentioned.
- Practice explaining your thought process clearly during coding interviews.
Conclusion:
The interview process was structured and thorough. I felt well-prepared for the technical rounds, but I could have practiced more behavioral questions for the HR round. My advice to future candidates is to balance technical preparation with soft skills and to ensure their resume is concise and impactful.
Company Name: American Express
Position: Data Scientist
Location: [Not specified]
Application Process: I applied via LinkedIn and was interviewed in July 2024.
Interview Rounds:
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Round 1 - Assignment Round:
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Questions Asked: Assignment on credit risk.
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Your Approach: I focused on understanding the problem statement thoroughly, applied relevant statistical and machine learning techniques, and ensured my solution was well-documented.
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Outcome: Passed this round.
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Round 2 - Technical Round:
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Questions Asked: Hyperparameter tuning.
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Your Approach: I explained various methods like GridSearchCV, RandomSearchCV, and Bayesian Optimization, along with their pros and cons. I also discussed how I would apply them in a real-world scenario.
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Outcome: Passed this round.
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Round 3 - Technical Round:
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Questions Asked: Case study for problem-solving.
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Your Approach: I structured my response by first understanding the problem, breaking it down into smaller parts, and then proposing a logical solution using data science techniques. I also discussed potential challenges and mitigation strategies.
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Outcome: Passed this round.
Preparation Tips:
- Focus on understanding core data science concepts like machine learning, statistics, and problem-solving.
- Practice case studies and assignments related to real-world business problems.
- Be thorough with hyperparameter tuning techniques and their applications.
Conclusion:
Overall, the interview process was rigorous but well-structured. The key to success was a strong grasp of fundamentals and the ability to apply them practically. I would advise future candidates to practice problem-solving and case studies extensively.
Company Name: American Express
Position: Data Scientist
Application Process: Applied through campus placement.
Interview Rounds:
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Round 1 - Resume Shortlist:
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Questions Asked: Resume review and shortlisting.
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Your Approach: Ensured my resume was concise and highlighted relevant skills and projects.
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Outcome: Successfully shortlisted for the next round.
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Round 2 - Coding Test:
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Questions Asked: Basic DP and array-related coding questions.
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Your Approach: Practiced common dynamic programming and array problems beforehand. Focused on optimizing solutions.
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Outcome: Cleared the coding test.
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Round 3 - One-on-One Round (Technical):
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Questions Asked: Resume walkthrough and discussion, followed by medium-level coding questions.
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Your Approach: Explained my projects clearly and solved the coding questions methodically.
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Outcome: Advanced to the next round.
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Round 4 - One-on-One Round (Manager Discussion):
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Questions Asked: Discussion with the hiring manager about my experience and problem-solving approach.
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Your Approach: Stayed confident and articulated my thought process clearly.
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Outcome: Positive feedback and moved to the final round.
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Round 5 - HR Round:
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Questions Asked: General HR questions about my background, career goals, and fit for the role.
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Your Approach: Answered honestly and aligned my responses with the company’s values.
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Outcome: Received the offer.
Preparation Tips:
- Focus on revising core data science concepts and coding problems, especially dynamic programming and arrays.
- Practice explaining your resume and projects clearly.
- Be prepared for behavioral questions and align your answers with the company’s culture.
Conclusion:
The interview process was thorough but well-structured. I felt prepared for the technical rounds, but I could have practiced more behavioral questions in advance. My advice to future candidates is to balance technical and soft-skills preparation and to stay confident throughout the process.
Company Name: American Express
Position: Data Scientist
Application Process: The application process involved a direct call from the manager for the initial round.
Interview Rounds:
-
Round 1 - Managerial Call (Technical):
- Questions Asked: The manager asked questions about machine learning concepts. The discussion lasted around 20 minutes.
- Your Approach: I focused on explaining the fundamentals of machine learning, including supervised and unsupervised learning, and shared examples of projects I had worked on.
- Outcome: Passed this round and moved to the next stage.
-
Round 2 - Coding Test:
- Questions Asked: Details about this round were not provided.
- Your Approach: N/A
- Outcome: N/A
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Round 3 - Technical Test:
- Questions Asked: Details about this round were not provided.
- Your Approach: N/A
- Outcome: N/A
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Round 4 - HR Interview:
- Questions Asked: Details about this round were not provided.
- Your Approach: N/A
- Outcome: N/A
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Round 5 - Onsite Interview:
- Questions Asked: Details about this round were not provided.
- Your Approach: N/A
- Outcome: N/A
Preparation Tips:
- Brush up on core machine learning concepts, including algorithms, model evaluation, and real-world applications.
- Practice coding problems, especially those related to data manipulation and analysis.
- Be prepared to discuss your past projects in detail, focusing on your contributions and problem-solving approach.
Conclusion:
The first round was a good start, and I felt confident discussing machine learning topics. For future rounds, I plan to focus more on coding practice and technical problem-solving. My advice to others is to thoroughly prepare for each stage and be ready to articulate your thought process clearly.