American Express Off Campus Drive 2026 | Analyst-Data Science | Gurugram

American Express is inviting applications for the Analyst – Data Science position in India, offering an opportunity for early-career professionals to build their careers in data science, analytics, machine learning, and quantitative problem-solving. The role can be particularly relevant for candidates with a background in statistics, economics, data science, mathematics, or other quantitative disciplines who are interested in applying analytical techniques to real-world business and risk-related challenges.

In this post, we cover the American Express Analyst – Data Science eligibility criteria, job responsibilities, resume preparation tips, expected selection process, company overview, and frequently asked questions. Candidates should carefully review the official job requirements before applying and ensure that their educational qualifications, skills, and experience match the current opening.

If you are looking for an early-career opportunity where you can strengthen your analytical and data science skills while gaining exposure to large-scale business problems, this American Express opening may be worth exploring.

) Job Summary

American Express is hiring for an Analyst – Data Science position in India. The opportunity is suitable for early-career professionals interested in data science, analytics, machine learning, statistical modelling, and data-driven business solutions.

According to the available job information, the role involves working with analytical and data-driven initiatives and may include activities such as model development or validation, researching analytical approaches, evaluating models, and communicating findings to business stakeholders. The listed experience requirement is 0–2 years in analytics or big-data workstreams.

Company: American Express
Position: Analyst – Data Science
Experience: 0–2 years
Employment: Full-time
Location: India; the available listing identifies Gurugram, Haryana
Work model: Depending on the position and business requirements, American Express supports different working arrangements.

Candidates should always verify the latest location, qualifications and other requirements on the official American Express careers page before applying.

2) Job Description

The Analyst – Data Science role offers an opportunity to apply analytical and quantitative skills to real-world business problems.

The available job description indicates that the position is associated with the Model Risk Management Group (MRMG) within American Express’s Global Risk and Compliance organization. The team is responsible for independent risk management of American Express models and certain non-model tools.

Depending on the assigned responsibilities, the candidate may be expected to:

Support the independent assessment and oversight of analytical and machine-learning models.
Work with AI and ML-based models used across areas such as marketing, credit, fraud and risk.
Assess model concepts, methodologies and intended uses.
Conduct analytical research and investigate opportunities to improve model effectiveness.
Identify gaps and contribute to stronger model-risk controls.
Apply statistical and quantitative techniques to business problems.
Review analytical approaches and communicate findings clearly.
Work with stakeholders and contribute to projects involving model validation and governance.
Stay informed about developments in machine learning, artificial intelligence and analytical methodologies.

This means the position is not simply about writing code. Candidates should be comfortable understanding why a model works, how it should be evaluated, what its limitations are, and how analytical findings can be communicated to stakeholders.

3) Eligibility Criteria

The available listing specifies 0–2 years of experience in analytics or big-data workstreams. It also mentions an MBA or master’s degree in areas such as Economics, Statistics or related quantitative fields, along with hands-on model development or validation experience.

Who Should Apply?

This opportunity may be suitable for candidates who:

Have a relevant postgraduate qualification in a quantitative discipline.
Have a strong foundation in statistics, mathematics, analytics or machine learning.
Have hands-on experience with model development, validation or analytical projects.
Can work with data and interpret quantitative results.
Enjoy solving structured and unstructured analytical problems.
Can explain technical findings in a clear and understandable manner.
Are comfortable collaborating with different teams.
Are interested in AI, machine learning and model-risk concepts.

Fresh graduates or early-career professionals should consider highlighting academic projects, internships, research work and independent projects where they demonstrate relevant skills.

Who May Not Be a Good Fit?

The role may be less suitable for candidates who:

Have little interest in quantitative or analytical work.
Prefer roles with minimal data or technical responsibilities.
Cannot demonstrate any practical exposure to analytics or modelling.
Are uncomfortable presenting or explaining analytical findings.
Prefer highly repetitive work rather than research and problem solving.

Meeting the stated qualifications does not guarantee selection, as hiring decisions depend on the company’s assessment and business requirements.

4) What Should Be Highlighted in the Resume?

Candidates should keep their resume focused on relevant evidence rather than simply listing technologies.

Important areas to highlight include:

Technical skills: Python, R, SQL, statistical modelling, machine learning and other relevant analytical tools.

Projects: Describe projects where you collected or processed data, developed a model, tested an approach, measured performance or generated business insights.

Model experience: If you have worked on model development or validation, clearly explain your contribution and methodology.

Academic/research work: Master’s dissertations, research projects and quantitative coursework can be valuable for early-career applicants.

Business impact: Wherever possible, explain what your analysis achieved—for example, improved prediction accuracy, reduced processing time or generated actionable insights.

Communication: Mention presentations, stakeholder interactions, research publications or projects where you translated technical findings for non-technical audiences.

Avoid adding skills simply because they appear in a job description. Your resume should accurately represent your actual knowledge and experience.

5) What Candidates Should Expect During the Recruitment Process?

American Express does not guarantee a single standardized selection process for every Data Science opening. The exact stages can vary according to the position, team and hiring requirements.

Candidates should nevertheless be prepared for some combination of:

Application and resume screening – The recruitment team reviews applications against the position requirements.
Initial recruiter interaction – Selected candidates may be contacted to discuss their background, interest and role suitability.
Technical or analytical assessment – Candidates may be tested on statistics, analytical thinking, SQL, programming, machine learning or relevant technical concepts.
Technical/project discussion – Be prepared to explain projects listed on your resume in considerable detail.
Behavioral and stakeholder discussion – Communication, teamwork, learning agility and problem-solving ability may be assessed.
Final evaluation and background verification – American Express states that employment offers are subject to successful background verification, subject to applicable laws and regulations.

Candidates should prepare by revising statistics, probability, machine learning fundamentals, SQL/Python and their own projects.

6) About the Company

American Express is a global payments and financial services company with a long history of innovation. Its business combines payments, financial products, customer relationships, technology and data.

American Express’s India careers information highlights data science as an important area of its analytics organization, including predictive modelling across different stages of the customer lifecycle.

The company also emphasizes values such as customer focus, respect for people, integrity, teamwork, innovation and continuous learning.

7) Why Join This Company?

For candidates building a career in analytics or data science, American Express can provide exposure to large-scale business problems where data and analytical models influence important decisions.

The company highlights career development, leadership opportunities, learning, inclusion and holistic well-being as parts of the employee experience. Its India careers information also describes areas such as data science, AI research and technology as part of its broader analytics ecosystem.

The role can therefore be particularly relevant to candidates who want to develop beyond basic data analysis and gain experience with machine learning, quantitative problem solving, model evaluation and business decision-making.

8) Frequently Asked Questions (FAQ)

Q1. Is this American Express Data Science role suitable for freshers?
The available listing specifies 0–2 years of experience, making it relevant to early-career candidates. However, applicants should verify the current requirements on the official job page before applying.

Q2. What educational background is preferred?
The listing mentions an MBA or master’s degree in Economics, Statistics or related fields. Candidates should check the official posting for the latest qualification requirements.

Q3. Is prior data science experience mandatory?
Hands-on model development or validation experience is specifically mentioned. Academic projects, internships and research work may help demonstrate practical capability, where relevant.

Q4. What skills should I prepare before applying?
Focus on statistics, machine learning fundamentals, analytical problem solving and relevant programming/data skills. Also be prepared to explain your projects clearly.

Q5. Is the job location in Bengaluru or Gurugram?
The job information available for this specific listing identifies Gurugram, Haryana. Since American Express has multiple analytics operations in India, candidates should rely on the location shown on the official application page for the position they are applying to.

Q6. Where should candidates apply?
Candidates should use the official American Express careers listing provided in the job announcement rather than relying on third-party application websites.

The Apply link is available in the “How to Apply?” section.

Q7. Does applying guarantee an interview?
No. Applications are subject to screening and the company’s recruitment process. Meeting the eligibility criteria does not guarantee an interview or employment.

How to Apply?

Interested candidates meeting the eligibility criteria have to apply at the following link: