Campus Graduate II Summer Internship Program - 2027 Data Science, Finance - New York, NY

Business Unit / Role Specific Information

At American Express, Finance Data Science & Analytics applies advanced modeling, machine learning, artificial intelligence, and statistical techniques to help the organization make scientifically grounded decisions around growth, risk, profitability, and enterprise strategy. Finance Decision Scientists work at the intersection of Finance, Business, Risk, and Technology to develop predictive models, analytical frameworks, and data-driven insights that inform senior management decisions and improve how we forecast, optimize, and manage the business.

This internship is designed for candidates who want to build and apply modeling capabilities to real-world business problems. Interns will work with large-scale datasets, develop and test predictive models, translate business questions into technical modeling approaches, and communicate insights in a clear, structured way. The role requires strong quantitative problem-solving, hands-on programming, and the ability to connect model outputs to business strategy and decision-making.

Team Responsibilities Include

• Develop predictive models and analytical frameworks that forecast key

top-line and financial metrics, supporting both short-term execution and long-term strategic planning.

• Apply machine learning, statistical modeling, and advanced analytics to

identify drivers of business performance, risk, customer behavior, and enterprise value.

• Build, validate, and interpret models that inform decisions related to

growth, profitability, credit performance, fraud, recessionary preparedness, and balance sheet management.

• Translate complex business problems into structured analytical questions,

modeling approaches, and measurable outcomes.

• Partner with Finance, Business, Risk, and Technology teams to embed

model-driven insights into strategic decision-making.

• Communicate modeling methodology, assumptions, results, and business

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