American Express Analyst Data Science Jobs 2026 | Freshers Apply
American Express Analyst Data Science Jobs 2026 | Freshers Apply
American Express is hiring for the position of Analyst – Data Science in Gurugram and Bengaluru. This opportunity is ideal for MBA graduates and candidates with Master’s degrees in Statistics, Economics, Computer Science, or related disciplines who are passionate about analytics, machine learning, business intelligence, and data-driven decision making.
The role is part of the Credit & Fraud Risk (CFR) Analytics & Data Science Center of Excellence, a team that drives profitable business growth by leveraging advanced analytics, predictive modeling, machine learning, and risk management solutions. Candidates will work on real-world business challenges impacting millions of customers while gaining exposure to industry-leading analytics and data science practices.
Quick Job Snapshot
| Company Name | American Express |
| Role | Analyst – Data Science |
| Qualification | MBA / Master’s Degree |
| Eligible Degrees | Economics, Statistics, Computer Science, Related Fields |
| Experience | 0–30 Months |
| Salary | ₹12 LPA – ₹20 LPA (Estimated) |
| Job Type | Full-Time |
| Work Mode | Hybrid |
| Department | Credit & Fraud Risk Analytics |
Role Overview
As an Analyst – Data Science at American Express, you will work on predictive modeling, risk analytics, fraud detection, machine learning solutions, and business intelligence initiatives. The role involves analyzing large datasets, identifying patterns, developing analytical solutions, and helping business teams make data-driven decisions.
You will collaborate with global stakeholders, leverage the power of the American Express network, and contribute to innovative risk and fraud management strategies. This role offers an exceptional opportunity to develop expertise in analytics, machine learning, financial services, and decision sciences.
Key Responsibilities
- Develop, deploy, and validate predictive models.
- Support business decision-making using analytics and data science.
- Analyze large datasets to generate actionable business insights.
- Create innovative solutions for risk, fraud, and marketing challenges.
- Leverage machine learning and big data technologies.
- Identify opportunities to improve business performance.
- Present analytical findings to leadership and stakeholders.
- Collaborate with cross-functional global teams.
- Support profitable growth through data-driven decisions.
- Research developments in analytics, finance, and payments industries.
- Work on customer targeting, underwriting, and risk management initiatives.
- Contribute to innovation in fraud prevention and customer experience.
Required Skills / Eligibility
- MBA or Master’s Degree in Economics, Statistics, Computer Science, or related fields.
- 0–30 months of experience in analytics.
- Strong analytical and problem-solving skills.
- Excellent communication and presentation abilities.
- Ability to work effectively in a team environment.
- Strong interpersonal and stakeholder management skills.
- Ability to work independently on complex business problems.
- Quick learning ability and innovative mindset.
- Capability to collaborate with global business partners.
- Interest in data science, machine learning, and business analytics.
Preferred Skills
- Machine Learning concepts.
- Predictive Modeling.
- Statistical Analysis.
- Business Analytics.
- Risk Analytics.
- Big Data technologies.
- Data Visualization.
- Financial Services domain knowledge.
Salary Insights
American Express has not officially disclosed the compensation package for this role. Based on similar Data Science Analyst positions offered by leading multinational financial institutions, candidates can expect an estimated annual salary between ₹12 LPA and ₹20 LPA, along with performance bonuses, healthcare benefits, learning programs, and career development opportunities.
Why This Role Is Good for Candidates
This role provides exposure to predictive analytics, machine learning, fraud risk management, business intelligence, customer analytics, and financial services. Candidates gain hands-on experience solving large-scale business problems while working with industry-leading analytics professionals. The skills developed can lead to careers in Data Science, Machine Learning Engineering, Business Analytics, Risk Analytics, Product Analytics, and AI-driven decision sciences.
Recommended Skills & Learning Resources
To strengthen your data science and analytics capabilities, consider the following resources:
Interview Preparation
Candidates should prepare for statistics, machine learning fundamentals, analytical problem-solving, business case studies, SQL concepts, and behavioral interviews. Review the Data Scientist Interview Questions for Freshers and the Complete Interview Guide for Freshers 2026 for effective preparation.
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- Click the Apply Now button below.
- Visit the official American Express Careers portal.
- Review the eligibility criteria and role requirements.
- Prepare an updated resume highlighting analytics, statistics, and project experience.
- Complete the online application form.
- Upload all required documents.
- Submit your application.
- Monitor your registered email for recruitment updates.
FAQ
Q1. Who can apply for the American Express Analyst – Data Science role?
Candidates with an MBA or Master’s Degree in Economics, Statistics, Computer Science, or related fields can apply.
Q2. Is prior experience mandatory?
No. Candidates with 0–30 months of analytics experience are eligible.
Q3. What are the primary areas of work?
The role focuses on predictive modeling, machine learning, fraud analytics, risk management, customer analytics, and business intelligence.
Final Thoughts
The American Express Analyst – Data Science role is one of the most attractive opportunities for aspiring analytics professionals. With exposure to large-scale datasets, predictive modeling, machine learning, and financial risk management, candidates can build highly valuable skills while working with one of the world’s leading financial services organizations. Continue developing your technical expertise, strengthen your analytical foundation, and regularly explore related opportunities to accelerate your data science career.