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American Express

Issues credit cards and runs a payment network

Director-Data Science - New York, NY

Data ScienceUpdated on Nov 11
Full-timeNew York, NY, USAIn-person

About the Job

Requirements

  • A bachelor's degree in a quantitative field such as Engineering, Computer Science, Mathematics, Statistics, or Economics.
  • Exceptional analytical and conceptual problem-solving ability, including experience structuring and solving complex, ambiguous business challenges using first-principles thinking.
  • Proven leadership experience managing and developing high-performing analytics or data science teams in a complex, cross-functional environment.
  • A strong technical foundation in modern data science and AI, with the ability to guide solution design, challenge technical approaches, and connect analytical choices to business outcomes.
  • The ability to influence senior stakeholders through clear, structured, and compelling communication and executive storytelling.
  • Experience driving large-scale analytics, Decision Science, or AI initiatives from concept through production, adoption, and ongoing optimization.
  • Strong commercial acumen and storytelling, with the ability to connect technical strategy, architecture, and investment choices to measurable business value.

Responsibilities

  • Define and lead the Decision Science roadmap across Global Servicing, identifying and prioritizing high-impact opportunities across servicing experiences, membership value and engagement, colleague efficiency, and operational excellence.
  • Translate business strategy into clear Decision Science priorities, success measures, and investment choices, connecting technical opportunities to measurable customer and business outcomes.
  • Provide technical leadership across AI and machine learning, generative AI, prediction, recommendation, optimization, experimentation, and learning, setting a high bar for solution design, evaluation, and analytical rigor.
  • Drive the evolution of GSDS toward emerging AI paradigms, including Agentic AI and multi-agent workflows, transformer-based recommenders and representation learning, and Conversational AI/LLM-based systems that reason over context and support intelligent servicing decisions.
  • Challenge architectures, modeling choices, evaluation frameworks, and trade-offs, and guide teams from experimentation through scalable production deployment.
  • Oversee development of reusable, production-ready data science products and decisioning systems leveraging customer, behavioral, contextual, and operational data for timely, personalized decisions.
  • Partner with Technology, platform, and governance teams to deploy and evolve AI solutions with appropriate architecture, observability, controls, model and AI quality, and lifecycle discipline.
  • Lead, mentor, and grow a high-performing team of data science professionals, fostering technical excellence, first-principles problem solving, innovation, ownership, and commercial judgment.
  • Build organizational capability across Applied GenAI, AI engineering and architecture, advanced recommendation and decisioning methods, strategic problem solving, and commercial storytelling.
  • Influence senior stakeholders and build cross-functional accountability, using clear narratives to drive prioritization, investment decisions, adoption, and execution of major Decision Science initiatives.
  • Evaluate advances in AI and Decision Science and translate relevant methods into practical, scalable capabilities and reusable best practices for Global Servicing.
  • Shape a forward-looking technical roadmap that strengthens GSDS capabilities in Agentic AI, Conversational AI, modern recommendation systems, experimentation, AI engineering, architecture, governance, and continuous optimization.

Desired Qualifications

  • A master's degree in a quantitative field such as Engineering, Computer Science, Mathematics, Statistics, or Economics.
  • Technical exposure to or experience driving modern AI capabilities such as Agentic AI and agentic workflows, transformer architectures and transformer-based recommendation systems, Conversational AI, large language models, and advanced personalization or next-best-action systems.
  • A strong understanding of the end-to-end Decision Science and AI lifecycle, including experimentation, model and AI evaluation, decision logic and orchestration, productionization, monitoring, governance, and continuous optimization.
  • A track record of building scalable data products, recommendation or decisioning systems, or AI-driven capabilities with measurable customer, revenue, productivity, cost, or control outcomes.
  • Experience driving transformation from traditional analytics toward AI-powered, productized Decision Science capabilities and reusable enterprise solutions.

About the Company

American Express

Issues credit cards and runs a payment network

American Express issues credit and pay-in-full charge cards to consumers and businesses, and unlike most card issuers it also runs the payment network its cards use. Its Travelers Cheque, launched in 1891, made carrying money abroad safer, and in 1959 it became the first major issuer of plastic cards. Cardholders earn points toward flights and hotels plus perks such as airport lounges, while companies use its corporate cards for employee travel and expenses. American Express earns a fee from merchants on each purchase, plus annual fees and interest on balances cardholders carry. Henry Wells and William Fargo, who later started Wells Fargo, co-founded it in 1850 as a freight delivery business; it is based in New York City.

Company size
10,001+
Company stage
IPO
Headquarters
New York City, New York
Founded
1850

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