Senior Analyst-Data Science (Machine Learning, LLM, GenAI)

The Global Servicing Decision Science (GSDS) team is the Decision Science engine for Global Servicing, bringing together advanced analytics, data science, AI/GenAI, and decisioning to transform servicing experiences, empower colleagues, and enable intelligent operations. GSDS serves as a centralized Decision Science Center of Excellence, partnering across Product/Capabilities, Control Management, Strategy, Operations, Technology, and enterprise analytics and data science teams.

GSDS builds and scales servicing intelligence across Global Servicing—from predictive and proactive servicing, personalization, next-best-action and journey orchestration to conversational AI, agent assist, AI-powered coaching, conduct and quality monitoring, and complaint and dispute intelligence. Our mandate is to build durable, production-ready capabilities that continuously improve decisions through prediction, recommendation, optimization, experimentation, and learning.

Role Description

As a Senior Analyst, you will develop and implement Decision Science solutions that help power the next generation of servicing intelligence at American Express. You will use data, machine learning, experimentation, and emerging AI/GenAI techniques to solve customer, colleague, and operational problems and translate analytical work into measurable business impact.

This role is ideal for someone with strong quantitative and coding skills who is excited to work hands-on with modern AI—including Agentic AI, transformer-based recommendation approaches, and Conversational AI—and to grow their technical depth while building production-oriented Decision Science capabilities.

- Master’s degree in a quantitative field (e.g., Engineering, Computer Science, Mathematics, Statistics, Economics, Finance).
- 2+ years of professional experience in Data Science, Machine Learning, Advanced Analytics, or a related quantitative field.
- Strong proficiency in Python, SQL, or similar analytical tools, with experience building machine learning models such as tree-based models, regression/classification models, clustering, or related techniques.
- Exposure to LLMs, GenAI, or modern deep-learning approaches and a strong interest in developing expertise in emerging AI capabilities.
- Strong analytical and conceptual thinking with the ability to solve unstructured and complex business problems.
- Strong written and verbal communication skills and ability to collaborate effectively with cross-functional partners.

Preferred Qualifications

- Hands-on exposure to Agentic AI/agentic frameworks, transformer architectures, transformer-based recommendation systems, Conversational AI, LLM applications, embeddings, or semantic modeling.
- Experience with personalization, recommendation systems, next-best-action, optimization, experimentation, or customer decisioning problems.
- Experience processing and analyzing large-scale structured or unstructured datasets and translating models into scalable analytical solutions.
- Familiarity with model/AI evaluation, productionization, monitoring, or MLOps/LLMOps concepts.
- Curiosity about customer servicing and the ability to connect technical work to measurable customer and business outcomes.