American Express is hiring Fresher & Experienced candidates for Analyst – Data Science. The details of the job, requirements and other information given below:
AMERICAN EXPRESS IS HIRING : ANALYST – DATA SCIENCE
- Qualification : Bachelors in Statistics/Mathematics/Economics/Engineering with relevant experience.
- Preferred: Post Graduation in Statistics/Mathematics/Economics/Engineering/Management.
- Work Experience: 0-18 months
- Strong analytical skills combined with excellent presentation and interpersonal skills.
- Hands on in modelling, ML, statistics and hypothesis testing techniques
- Hands on with data mining, cleansing, extraction/transformation to enable analysis, interpretation, and insights
- Working knowledge of Python, MLS, PySpark, SQL, Hive.
- Working knowledge of data visualization tools like Tableau and Power BI will be advantage.
- Location: Gurugram, Haryana, India
Don’t miss out, CLICK HERE (to apply before the link expires)
Interview Questions and Answers – American Express (Data Science & Analytics Role)
1. Tell me about yourself.
Answer:
I have completed my [Bachelor’s/Postgraduate degree] in [Statistics / Economics / Engineering / Mathematics] from [Your University]. I enjoy working with data to solve real problems and help businesses grow. I have basic experience with Python, SQL, and data analysis techniques through [college projects / internships / self-learning]. I’m also interested in data visualization using tools like Power BI or Tableau. I’m now excited to start my career at American Express because I admire the company’s focus on innovation, global impact, and teamwork.
2. Why do you want to work at American Express?
Answer:
I want to work at American Express because it’s a globally respected company that values data and uses it to make smart business decisions. I’m impressed by how Amex supports its customers and employees and creates a culture of learning and growth. I also like that this role offers the chance to work on important areas like forecasting, marketing analytics, and investment planning, which match my skills and career interests.
3. What do you understand about this role?
Answer:
This role is part of the Data Science and Analytics team, which helps the company make better business decisions using data. The job involves forecasting key financial and marketing metrics, analyzing investments, building reports, and working with different teams to improve results. It also includes using tools like SQL, Python, and visualization tools to turn data into insights that support growth.
4. What experience do you have with data analysis or data science?
Answer:
I have worked on projects in college and through online courses where I used tools like Python and SQL for analyzing data. For example, I did a project where I used data from different sources, cleaned it, and created visual reports to show patterns and trends. I’ve also learned how to use basic machine learning techniques and statistical models like regression, clustering, and hypothesis testing. I enjoy solving problems with data and always try to find insights that are useful.
5. What is hypothesis testing and why is it used in data analysis?
Answer:
Hypothesis testing is a statistical method used to decide if there is enough evidence in a sample of data to support a certain idea or assumption about a population. For example, if we want to know whether a new marketing strategy leads to higher sales, we can use hypothesis testing to check if the sales difference is real or just due to chance. It helps in making decisions based on data, not guesses.
6. Can you explain a machine learning model you’ve worked on or studied?
Answer:
Yes. I worked on a classification project where I used a decision tree model to predict whether a customer would buy a product or not, based on their past behavior. I used Python with libraries like scikit-learn to build and test the model. I cleaned the data, split it into training and testing sets, trained the model, and evaluated the results using accuracy and confusion matrix. It helped me understand how machine learning can be used in real business situations.
7. How would you handle a situation where a stakeholder requests a report urgently, but you don’t have all the data?
Answer:
First, I would talk to the stakeholder to understand what exactly they need and how urgent it is. Then I would check what data is available and provide a partial or temporary report using the data I have, while clearly mentioning what’s missing. I would also start working on getting the missing data and give an estimated time for the complete report. Communication and transparency are very important in such situations.
8. Which tools and programming languages are you familiar with?
Answer:
I am comfortable with:
Python – for data cleaning, analysis, and machine learning
SQL – for writing queries and extracting data from databases
Excel – for basic analysis and reporting
Tableau / Power BI – for data visualization
I’ve also started learning PySpark and cloud basics like AWS, and I’m excited to improve my skills further.
9. How do you ensure your analysis is accurate and useful?
Answer:
I follow a step-by-step process:
I check the quality of data and clean it before analysis.
I use clear logic and double-check calculations.
I validate the results by comparing them with past trends or other reliable sources.
I keep my analysis simple and focus on key insights that can help make better decisions.
Finally, I review the results with teammates or mentors before sharing.
10. Tell me about a time you worked in a team or handled a difficult problem.
Answer:
During my college final year project, I worked in a team of 4 to analyze customer data for a retail business. We faced challenges with messy data and different opinions in the team. I took the lead in cleaning the data and suggested dividing tasks based on our strengths. We set up daily check-ins to stay on track. In the end, we delivered a dashboard that showed valuable trends in customer behavior, and our professor appreciated the teamwork and insights.
Bonus Tips for Students and Job Seekers:
Practice Python and SQL regularly on platforms like HackerRank or LeetCode.
Build a small data analysis project and post it on GitHub or LinkedIn.
Review basic statistics and ML models like regression, classification, clustering.
Learn to explain technical work in simple words, especially in interviews.
Read about American Express’s values and recent initiatives to show you’re interested.
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