American Express is a global financial services company operating across banking, finance, and B2B payments. Its business includes consumer, business, and corporate credit cards; payment and merchant services; savings accounts, certificates of deposit, and personal loans; as well as rewards, travel programs, corporate payment solutions, financial education, security services, and cardmember and merchant support.
Director of Data Science, Global Servicing — American Express (Hybrid, India)
Lead American Express Global Servicing’s AI-powered Decision Science strategy. Build production-ready recommendation, GenAI, and decisioning capabilities while developing high-performing data science teams.
Description
- Set and lead the Global Servicing Decision Science roadmap
- Find and rank high-value opportunities across servicing, membership value and engagement, colleague productivity, and operational excellence
- Convert business strategy into Decision Science priorities, success metrics, and investment decisions
- Provide technical direction across AI/ML, GenAI, prediction, recommendation, optimization, experimentation, and learning
- Advance Agentic AI, multi-agent workflows, transformer-based recommenders, representation learning, and Conversational AI/LLM systems
- Assess architectures, modeling approaches, evaluation frameworks, and technical trade-offs
- Lead teams from early experimentation to scalable production deployment
- Oversee reusable, production-ready data science products and decisioning systems built on customer, behavioral, contextual, and operational data
- Collaborate with Technology, platform, and governance partners on architecture, observability, controls, model and AI quality, and lifecycle practices
- Hire, mentor, and develop a high-performing data science organization
- Strengthen organizational capabilities in Applied GenAI, AI engineering and architecture, recommendation and decisioning, strategic problem solving, and commercial storytelling
- Engage senior stakeholders to guide prioritization, investment, adoption, and execution
- Monitor advances in AI and Decision Science and convert relevant techniques into scalable capabilities and reusable practices
- Define the technical roadmap for Agentic AI, Conversational AI, recommendation, experimentation, AI engineering, architecture, governance, and continuous optimization
Requirements
- Bachelor’s degree in a quantitative discipline such as engineering, computer science, mathematics, statistics, or economics
- Exceptional analytical and conceptual problem-solving skills, including first-principles approaches to complex and ambiguous business challenges
- Demonstrated experience leading and developing high-performing analytics or data science teams in complex, cross-functional organizations
- Strong technical grounding in contemporary data science and artificial intelligence
- Ability to shape solution design, question technical approaches, and link analytical decisions to business results
- Ability to influence senior stakeholders through clear, structured communication and persuasive executive storytelling
- Experience taking large-scale analytics, Decision Science, or AI initiatives from concept through production, adoption, and ongoing improvement
- Strong commercial judgment and storytelling skills, connecting technical strategy, architecture, and investment decisions to measurable business value
- Master’s degree in a quantitative discipline preferred
- Technical familiarity with Agentic AI and agentic workflows, transformer architectures and recommendation systems, Conversational AI, LLMs, and advanced personalization or next-best-action solutions preferred
- Strong understanding of the complete Decision Science and AI lifecycle, including experimentation, model and AI evaluation, decision logic and orchestration, productionization, monitoring, governance, and continuous optimization preferred
- Record of delivering scalable data products, recommendation or decisioning systems, or AI-enabled capabilities with measurable results preferred
- Experience moving organizations from traditional analytics to AI-powered, productized Decision Science capabilities and reusable enterprise solutions preferred
Benefits
- Competitive base compensation
- Bonus opportunities
- Support for financial wellness and retirement planning
- Comprehensive medical, dental, vision, life insurance, and disability coverage, subject to location
- Flexible hybrid, onsite, or virtual work arrangements based on the role and business needs
- Generous paid parental leave, subject to location
- Complimentary access to global onsite wellness centers staffed by nurses and doctors, subject to location
- Confidential counseling through the Healthy Minds program
- Career development and training opportunities