Analyst-Data Science

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.

How will you make an impact in this role?

As an Analyst, you will contribute to the development and implementation of 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 while building your ability to translate analytical work into measurable business impact.

This role is ideal for someone with strong quantitative and coding foundations who is excited to work hands-on with modern AI—including Agentic AI, transformer-based recommendation approaches, and Conversational AI—and to develop their technical depth through practical Decision Science applications.

Minimum Qualifications

- Master’s degree in a quantitative field (e.g., Engineering, Computer Science, Mathematics, Statistics, Economics, Finance).
- Relevant academic, internship, or professional experience in Data Science, Machine Learning, Advanced Analytics, or a related quantitative field.
- Proficiency in Python, SQL, or similar analytical tools, with foundational experience building machine learning models such as tree-based models, regression/classification models, clustering, or related techniques.
- Exposure to or coursework/project experience with LLMs, GenAI, or modern deep-learning approaches, with strong interest in developing expertise in emerging AI capabilities.
- Strong analytical and conceptual thinking with the ability to structure problems, learn quickly, and work through ambiguity.
- Strong written and verbal communication skills and the ability to work effectively in collaborative, cross-functional teams.

Preferred Qualifications

- Exposure through professional work, internships, research, or academic projects to Agentic AI/agentic frameworks, transformer architectures, transformer-based recommendation systems, Conversational AI, LLM applications, embeddings, or semantic modeling.
- Exposure to personalization, recommendation systems, next-best-action, optimization, experimentation, or customer decisioning problems.
- Experience or project exposure working with structured or unstructured datasets and developing analytical or machine learning solutions.
- Familiarity with model/AI evaluation and basic concepts related to productionization, monitoring, or MLOps/LLMOps.
- Curiosity about customer servicing and the ability to connect technical work to measurable customer and business outcomes.