American Express is recruiting for a Data Science Analyst position focused on analytics, data science, machine learning and risk management. The role is associated with the Credit and Fraud Risk Analytics & Data Science function and is listed for Gurugram, Haryana and Bengaluru, Karnataka.
The position is relevant to candidates who want to work with large datasets and apply statistical analysis, predictive modelling and machine learning to business problems such as credit risk, fraud detection and customer analytics.
According to the recruitment information available for the position, candidates with 0–30 months of relevant experience and a relevant MBA or master’s-level quantitative background can be considered. The listed technical skills include Python, SAS, R, SQL, Hive, Spark and Machine Learning.
The reported application deadline is August 19, 2026. Candidates should verify that the vacancy remains active and confirm the latest requirements through the official American Express careers portal before applying.
1. Quick Facts
| Particulars | Details |
|---|---|
| Company | American Express |
| Job Role | Data Science Analyst |
| Associated Position | Credit and Fraud Risk Analytics & Data Science |
| Job ID | 26012381 |
| Job Category | Data Management & Analytics |
| Location | Gurugram, Haryana & Bengaluru, Karnataka |
| Work Mode | Hybrid |
| Qualification | MBA / Master’s degree in a relevant quantitative or analytical field |
| Experience | 0–30 months |
| Employment Type | Full-time |
| Salary | Not specified in the available recruitment information |
| Work Schedule | Day shift, according to the supplied job information |
| Key Technologies | Python, SAS, R, SQL, Hive, Spark, Machine Learning |
| Posted On | August 10, 2026 |
| Last Date to Apply | August 19, 2026, according to the supplied recruitment information |
Important: The application deadline and vacancy status can change. Verify the live position on the American Express careers portal before applying.
2. Fresher Preparation Checklist
Candidates preparing for this Data Science Analyst position should focus on quantitative analysis, programming and business-oriented problem solving.
- Revise Python fundamentals
- Practice SQL queries and joins
- Understand statistics and probability
- Revise machine learning fundamentals
- Learn data preprocessing techniques
- Understand model evaluation metrics
- Review fraud and credit-risk concepts
- Practice exploratory data analysis
- Revise basic Spark concepts
- Understand Hive and large-scale data processing
- Review SAS or R if listed on your resume
- Practice explaining analytical findings
- Prepare your academic and personal projects
- Update your resume with relevant analytics skills
- Verify the experience and education requirements
3. About American Express
American Express is a global payments and financial services company operating across areas such as payments, lending, travel and financial services. The company uses technology and data extensively across its products and business operations.
Data and analytics are particularly important within financial services because companies need to make informed decisions around transactions, customers, credit exposure and potential fraud.
For a Data Science Analyst, this creates an opportunity to work on analytical problems where statistical modelling and machine learning can support business and risk-related decisions.
The role therefore combines technical data skills with an understanding of business outcomes rather than focusing exclusively on model development.
4. About the Data Science Analyst Role
The Data Science Analyst position focuses on using data and analytical techniques to help solve business problems within the Credit and Fraud Risk Analytics & Data Science function.
The work can involve analysing large datasets, identifying patterns, developing analytical solutions and communicating findings to stakeholders.
Potential areas of application include:
- Credit risk
- Fraud detection
- Customer analytics
- Predictive modelling
- Risk analytics
- Business decision support
Unlike a purely software-development position, this role places greater emphasis on statistics, data analysis, modelling and communicating insights.
Candidates should therefore be comfortable moving between technical analysis and business-oriented questions.
5. Responsibilities
Based on the recruitment information supplied for the position, the work can involve areas such as:
Data Analysis
- Analyse structured and large-scale datasets
- Identify patterns and relationships in data
- Perform exploratory data analysis
- Prepare data for analytical modelling
- Investigate data quality issues
Data Science & Modelling
- Apply statistical and machine learning techniques
- Develop analytical solutions for business problems
- Evaluate model performance
- Compare analytical approaches
- Support predictive modelling activities
Risk & Fraud Analytics
- Analyse patterns associated with credit risk
- Support fraud-related analytical investigations
- Identify potentially unusual behaviour in datasets
- Develop insights that can support risk decisions
Business Communication
- Present analytical findings
- Translate technical results into understandable insights
- Work with business and cross-functional teams
- Document analytical approaches and findings
The exact responsibilities can vary according to the team and project assigned to the selected candidate.
6. Why This Role Is Useful for Freshers
For candidates entering analytics, the position can provide exposure to the complete analytical problem-solving process.
| Area | What a Fresher Can Learn |
|---|---|
| Python | Data analysis and analytical programming |
| SQL | Querying and manipulating business data |
| Statistics | Quantitative decision-making |
| Machine Learning | Predictive modelling fundamentals |
| Risk Analytics | Applying data science to financial risk |
| Fraud Analytics | Detecting unusual patterns |
| Big Data | Working with Hive and Spark |
| Business Analytics | Turning data into useful recommendations |
| Communication | Presenting technical findings to stakeholders |
Possible Career Directions
Possible career directions include:
- Data Analyst
- Data Scientist
- Risk Analyst
- Fraud Analytics Specialist
- Machine Learning Analyst
- Risk Modeler
- Business Analyst
- Analytics Consultant
- Machine Learning Engineer
- Senior Data Scientist
These are possible career directions and are not guaranteed promotions or outcomes from the role.
8. Eligibility Criteria
| Requirement | Details |
|---|---|
| Degree | MBA / Master’s degree in a relevant field |
| Relevant Background | Economics, Statistics, Computer Science or another quantitative/analytical discipline |
| Experience | 0–30 months |
| Programming | Python, SAS or R |
| Database / Data | SQL, Hive |
| Big Data | Spark |
| Analytics | Machine Learning and statistical analysis |
| Location | Gurugram or Bengaluru |
| Work Mode | Hybrid |
| Work Schedule | Day shift, according to the supplied information |
Candidates should verify the complete eligibility criteria against the live job listing before applying.
9. Skills Required
Programming
- Python
- SAS
- R
Candidates do not necessarily need equal proficiency in every language. Focus on the languages explicitly required by the live job description and the ones you can genuinely explain.
Database & Data
- SQL
- Data manipulation
- Data cleaning
- Exploratory data analysis
- Hive
Big Data
- Apache Spark
- Distributed data processing
- Large-scale data analysis
Statistics
- Probability
- Descriptive statistics
- Hypothesis testing
- Correlation
- Regression
- Statistical inference
Machine Learning
- Supervised learning
- Unsupervised learning
- Classification
- Regression
- Feature engineering
- Model evaluation
- Overfitting and underfitting
Risk & Fraud Analytics
- Credit risk fundamentals
- Fraud detection
- Anomaly detection
- Predictive risk modelling
- Customer behaviour analysis
Professional Skills
- Analytical thinking
- Problem solving
- Business communication
- Data storytelling
- Team collaboration
- Attention to detail
10. Preparation Tips
Step 1: Strengthen SQL
Practice:
- SELECT
- WHERE
- GROUP BY
- HAVING
- ORDER BY
- JOINs
- Subqueries
- CTEs
- Window functions
For a data-focused position, SQL is not a decorative skill on the resume. You should actually be able to use it.
Step 2: Revise Statistics
Focus on:
- Mean, median and standard deviation
- Probability
- Distributions
- Correlation
- Hypothesis testing
- Confidence intervals
- Regression
- Sampling
Be prepared to explain why a particular statistical method is appropriate.
Step 3: Prepare Machine Learning
Revise:
- Linear regression
- Logistic regression
- Decision trees
- Random forests
- Clustering
- Feature engineering
- Train/test split
- Cross-validation
- Precision
- Recall
- F1-score
- ROC-AUC
Fraud detection often involves imbalanced datasets, so understand why accuracy alone may not be a useful evaluation metric.
Step 4: Learn Risk Analytics Fundamentals
Understand the basic ideas behind:
- Credit risk
- Fraud risk
- Risk scoring
- Anomaly detection
- False positives
- False negatives
- Predictive risk models
You do not need to become a banking expert before applying, but understanding the business context can make your technical answers much stronger.
Step 5: Review Spark and Hive
Learn the fundamentals of:
- Distributed data processing
- Spark DataFrames
- Spark SQL
- Basic transformations
- Hive tables
- Large-scale data workflows
If these technologies are on your resume, be prepared to explain where and why you used them.
Step 6: Prepare Your Projects
Choose one or two projects and prepare to explain:
- The business problem
- The dataset
- Data preprocessing
- Exploratory analysis
- Model selection
- Evaluation metrics
- Results
- Limitations
- Possible improvements
Your explanation should show that you understand the decisions you made, not merely that you followed a tutorial.
Step 7: Resume Preparation
- Highlight data science projects
- Mention Python and SQL prominently if genuinely skilled
- Include relevant machine learning projects
- Add statistics or analytics coursework where useful
- Mention Spark/Hive only if you have practical exposure
- Remove technologies you cannot discuss
- Keep the resume concise
11. Work Schedule
Work mode: Hybrid.
Locations: Gurugram, Haryana and Bengaluru, Karnataka.
The supplied recruitment information states that the position follows a day shift. Exact working hours and hybrid-office requirements should be confirmed with American Express during recruitment.
12. Salary
Salary: Not specified in the available recruitment information.
No verified salary figure has been provided for this position.
Compensation can vary based on role, location, qualifications and experience. Candidates should confirm the actual compensation through the official American Express recruitment process.
13. Selection Process
The specific selection process for Job ID 26012381 has not been independently confirmed from the official job listing available to us.
Possible Selection Process
Candidates may encounter stages such as:
- Resume screening
- Online assessment or analytical test
- Technical interview
- Data science / case-based discussion
- Hiring manager discussion
- HR discussion
- Document verification
- Offer and onboarding
Note: These are possible recruitment stages and are not confirmed for every candidate. The actual process may vary.
Interview Topics Worth Preparing
- SQL
- Python
- Statistics
- Probability
- Machine learning
- Data preprocessing
- Model evaluation
- Fraud detection
- Credit risk
- Case studies
- Your data science projects
14. How to Apply
Application Steps
- Visit the official American Express careers portal.
- Search for Data Science Analyst.
- Verify Job ID 26012381.
- Check the location and current vacancy status.
- Review the latest eligibility requirements.
- Prepare your updated resume.
- Complete the online application.
- Submit the application through the official careers portal.
- Save your application details.
Official American Express Careers:
Important: The supplied recruitment information lists August 19, 2026 as the application deadline. Because job postings can close early or change, verify the live listing before submitting your application.
15. Important Points Before Applying
- Confirm your degree matches the requirement.
- Check the 0–30 months experience condition.
- Verify whether your educational background is accepted.
- Check the Gurugram/Bengaluru location.
- Confirm the hybrid work arrangement.
- Prepare SQL and Python thoroughly.
- Revise statistics and machine learning.
- Understand basic credit and fraud analytics.
- Make sure your resume reflects your actual skills.
- Verify Job ID 26012381.
- Check whether the application is still open.
- Apply through the official American Express careers portal.
- Never pay anyone for a job opportunity.
16. FAQs
Is the American Express Data Science Analyst role suitable for freshers?
The listed experience range is 0–30 months, making the position relevant to early-career candidates. However, the educational requirement includes an MBA or master’s-level relevant qualification, so candidates should carefully check the complete eligibility criteria.
What qualification is required?
The supplied recruitment information lists an MBA or master’s degree in a relevant analytical or quantitative field, including areas such as Economics, Statistics and Computer Science.
What programming languages are required?
The listed technical skills include Python, SAS and R, along with SQL, Hive and Spark.
Is SQL required for the Data Science Analyst role?
Yes. SQL is among the key technical skills listed for the position. Candidates should be comfortable with joins, aggregations, filtering and more advanced query concepts.
Is machine learning required?
Machine learning is included among the key skills for the role. Candidates should understand fundamental algorithms, feature preparation and model evaluation.
Does the role involve fraud detection?
The position is associated with Credit and Fraud Risk Analytics & Data Science, so fraud and risk analytics are important areas of the role.
What is the salary?
The available recruitment information does not specify a salary figure. Candidates should confirm compensation through American Express.
Is the job work from home?
The position is listed as hybrid for Gurugram and Bengaluru. The exact number of remote or office days is not specified in the available information.
17. Related Job Opportunities
Candidates interested in data science, AI and analytics careers can also explore:
- [L&T GenAI Trainee Recruitment 2026]
- [Carrier Associate – AI & Data Engineering Recruitment 2026]
- [Emerson Technology Internship 2026]
- [WEX Software Development Engineer 1 Recruitment 2026]
These should be inserted as genuine WordPress internal links once the related articles are published.
18. Simple Technology Workflow
A simplified data science workflow for a risk or fraud analytics use case can look like this:
Business Problem
↓
Data Collection
↓
SQL / Hive Data Extraction
↓
Data Cleaning & Exploration
↓
Feature Engineering
↓
Machine Learning Model
↓
Model Evaluation
↓
Risk / Fraud Prediction
↓
Business Decision & Monitoring
Note: This is a simplified educational workflow based on technologies and analytical concepts relevant to the role. It is not American Express’s internal architecture or decision-making system.
19. Final Thoughts
The American Express Data Science Analyst position can be a strong option for candidates who want to combine programming, statistics, machine learning and business analytics.
The role is particularly relevant to candidates interested in credit risk, fraud analytics and predictive modelling, where data science is used to solve practical financial-services problems.
Candidates should focus their preparation on SQL, Python, statistics, machine learning, data analysis and risk analytics. Experience with tools such as SAS, R, Hive and Spark can also be valuable where relevant to the role.
The reported application deadline is August 19, 2026, so candidates should verify the current vacancy and submit their application through the official American Express careers channel if the position is still open.
20. Disclaimer
Disclaimer: This article is provided for informational purposes based on the available recruitment information. Job availability, eligibility requirements, salary, locations, work arrangements and selection procedures may change. MahaboardSolutions is not the employer and does not guarantee selection or employment. Candidates should verify the latest information through the company’s official careers portal before applying.