American Express—Senior Manager- Risk Management

Job description

Description: How we serve our customers is constantly evolving and is a challenge we gladly accept. Whether you’re finding new ways to prevent identity fraud or enabling customers to start a new business, you can work with one of the most valuable data sets in the world to identify insights and actions that can have a meaningful impact on our customers and our business. And, with opportunities to learn from leaders who have defined the course of our industry, you can grow your career and define your own path. Find your place in risk and analytics on #TeamAmex. How will you make an impact in this role? The Credit Risk Team helps enable profitable business growth by managing credit risk and maintaining portfolio resiliency within established risk appetite. You will work in a highly dynamic and exciting environment, with the opportunity to shape credit-risk analytics and apply emerging capabilities such as Generative AI and Agentic AI to improve risk identification, portfolio monitoring, and analytical processes. The role will involve collaborating with teams across Risk, AI/ML, Technology, and the Business to develop insights and implement scalable solutions. To succeed in this role, you will need a strong blend of analytical and technical skills, innovative thinking, problem-solving ability, and relationship-building skills. Requirements: - 4+ years of relevant experience in Credit Risk Management, Portfolio Risk Analytics, Credit Strategy, or a similar risk-focused role, with demonstrated experience analyzing portfolio performance and developing risk insights or strategies. - Degree in a quantitative field such as Mathematics, Statistics, Economics, Computer Science, Engineering, or a related discipline. - Strong understanding of credit-risk concepts, portfolio performance, delinquency and loss trends, risk segmentation, early-warning indicators, and strategy evaluation. - Strong quantitative and analytical skills, with experience analyzing large and complex datasets, identifying key risk drivers, and translating findings into clear, actionable recommendations. - Experience developing or evaluating credit-risk strategies using internal, external, and alternative data sources, with the ability to assess incremental value and business impact. - Working knowledge of Generative AI, Large Language Models, and Agentic AI concepts, with the ability to identify and apply relevant use cases within credit-risk analytics and processes; hands-on experience with AI-enabled analytical solutions is a plus. - Strong problem-solving and cross-functional collaboration skills, with the ability to translate credit-risk business problems into analytical or technology requirements and partner effectively across Risk, AI/ML, Technology, Product, and external vendors. - Demonstrated ability to work independently, communicate effectively with senior stakeholders, and mentor and develop team members in a dynamic, matrixed environment. - Should be comfortable with rotational shift of 12.00pm to 09.00pm, 11.00am to 08.00pm, 08.00am to 05.00pm Responsibilities: - Identify and develop GenAI and Agentic AI use cases for credit-risk analytics and processes, including analysis of structured and unstructured information, automated research, identification of risk drivers, and generation of actionable portfolio insights. - Partner with AI/ML and Technology teams to prototype, test, and implement GenAI- and Agentic-AI-enabled solutions, translating credit-risk business problems into analytical requirements, assessing impact, conducting UAT, and supporting implementation. - Drive automation and productivity improvements across credit-risk analytics processes by applying GenAI and Agentic AI to repetitive analytical workflows, data gathering, portfolio monitoring, and investigative analysis. - Perform portfolio analytics across Commercial portfolios to identify emerging risk trends, deterioration patterns, concentrations, and opportunities to improve portfolio performance. - Evaluate external and third-party data sources, including news and market intelligence, to assess their relevance and incremental value for portfolio analytics, risk identification, and strategy development. - Monitor portfolio performance and conduct ongoing analytical reviews of risk trends, strategy effectiveness, and emerging areas of concern, providing insights and recommendations to business and risk partners.