Campus Graduate I Summer Internship Program - 2027 Global Decision Science, Credit & Fraud Risk - New York, NY

Business Unit/Role Specific Information

Credit and Fraud Risk (CFR) is a global function, responsible for making the right credit and fraud risk decisions that uphold operational excellence, drive growth, and accelerate innovation across American Express. CFR combines technology and infrastructure with new computing techniques to make better risk decisions and provide real-time customer communications and fraud  servicing.

Qualifications

Minimum Qualifications:

• Currently enrolled in a Master's degree program with an expected graduation date between December 2027 and June 2028.
• Pursuing a Master’s degree in Computer Science, Statistics, Data Science, Mathematics, Artificial Intelligence, or a related quantitative field.
• Proficiency in Python for data analysis, modeling, and automation.
• Experience working with large datasets using SQL, Hive, or similar technologies and/or developing GenAI, NLP, LLM, or AI-powered solutions.
• Hands-on experience applying statistical, machine learning, or AI techniques to solve business or research problems.
• Strong analytical, problem-solving, communication, and teamwork skills.

Preferred Qualifications

• Published research, patents, academic publications, conference presentations, or other significant analytical project work.
• Employment eligibility to work with American Express in the United States is required as the company will not pursue visa sponsorship for these positions.

Responsibilities

• How will you make an impact in this role?
• The Data Science Intern will contribute to the development of predictive models and analytical solutions that inform key business decisions across credit, fraud, and marketing. By applying statistical, machine learning, and AI techniques to complex datasets, they will generate insights that enhance customer experience, strengthen risk management, and support innovation across the enterprise.
• As an intern your responsibleness will include: Query, analyze, and derive insights from large and complex datasets using tools such as SQL, Hive, Python, and other analytical technologies.
• Build, test, and evaluate predictive models using machine learning, statistical techniques, and advanced analytical methods.
• Apply analytical approaches to solve business problems and support data-driven decision-making.
• Leverage large-scale data assets and/or develop GenAI-enabled solutions to address business challenges.
• Partner with data scientists, business partners, and cross-functional teams to deliver analytical solutions and recommendations.
• Communicate project findings and insights through clear presentations and visualizations.
• Contribute to projects that enhance risk management, customer experiences, operational efficiency, and business performance.