Campus Graduate Masters Full-Time Manager Onboarding - 2027 Global Decision Science, Credit & Fraud Risk - New York, NY

Business Unit/Role Specific Information The Credit and Fraud Risk (CFR) team at American Express employs 3,500 global professionals focused on managing credit, fraud, and banking risk, optimizing risk trade-offs while delivering exceptional customer experiences. CFR’s work spans the entire customer lifecycle, supporting core processes like credit underwriting, fraud detection, servicing, and product development while also maintaining regulatory compliance and operational integrity. CFR leverages advanced analytics, data science, and AI/ML techniques to drive risk modeling, personalize decisions, and enhance customer experiences. As a Data Science Manager, you will support the development of predictive models used across credit, fraud, and marketing to inform key business decisions. They apply statistical techniques and machine learning to real-world datasets, contributing to innovation in areas like customer personalization, risk management, and regulatory compliance. How will you make an impact in this role? Query and manipulate large datasets using tools like SQL, Hive, and Python. Build and test predictive models using machine learning techniques (e.g., logistic regression, decision trees, clustering). Document modeling choices and provides rationale for algorithm selection. Translate business goals into technical requirements and analytical questions. Develop a clear and structured final presentation that communicates the project’s purpose, methods, insights, and business impact. Analyze current decisioning models, develop and implement a plan to improve model prediction accuracy. Improve automation of customer connects via a GenAI enabled chatbot tool. Use deep learning to uncover hidden trends in fraud and credit bust-out. Programming (Python or R) – Writing scripts for data analysis, modeling, and automation. Minimum Qualifications: Master's Degree Candidate with 3+ years of relevant professional experience Effective team player with interpersonal skills; capable of working autonomously or collaboratively in cross-functional teams. Proficiency in the following skills: Programming (Python or R) for data analysis, modeling, and automation; SQL/Hive – Querying large-scale datasets efficiently. AI Techniques – Experimenting with modern AI methods, including neural networks and natural language processing. Machine Learning Hands-on experience in machine learning algorithm development or application Statistical Analysis – Interpreting data using descriptive and inferential statistics. Cloud & Big Data Tools – Basic experience with cloud platforms and distributed data systems. Strategic mindset, that connects work to broader strategy and suggest ideas to enhance scalability and impact Preferred Qualifications Master’s degree candidate with an expected graduation date between December 2026 and June 2027 Master’s degree in one of the areas below or related field: Computer Science Statistics Data Science Mathematics Artificial Intelligence Prior experience, taking initiative to establish or lead an on-campus student organization Proven deep analytical skills with the ability to design new decisioning models or develop innovative tools Skilled in delivering presentation to wide-ranging audience Employment eligibility to work with American Express in the United States is required as the company will not pursue visa sponsorship for these positions