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.

Decision Science & Modeling

• Contribute to analytical solutions, predictive models, recommendation and decisioning approaches that improve servicing experiences, personalization, colleague effectiveness, and operational outcomes.
• Explore and analyze customer, behavioral, interaction, and operational datasets to identify patterns and opportunities, engineer features, test hypotheses, and translate findings into actionable insights and solutions.

AI / GenAI & Experimentation

• Develop and evaluate machine learning and GenAI solutions using established modeling, experimentation, and validation techniques, with guidance as needed.
• Gain hands-on experience and contribute to emerging capabilities such as Agentic AI and agentic workflows, transformer-based recommendation systems, Conversational AI/LLM applications, and advanced personalization or next-best-action solutions.
• Support the design and analysis of experiments and performance measurements to assess model/AI quality, business impact, and opportunities for improvement.

Implementation & Partnership

• Write high-quality analytical code and work with data, Technology, and platform partners to support implementation, productionization, monitoring, and enhancement of Decision Science solutions.
• Build effective cross-functional relationships, communicate analytical findings clearly, and translate technical results into insights and recommendations.

Innovation & Technical Growth

• Build knowledge of advances in machine learning, GenAI, recommendation systems, and decision intelligence; test relevant methods and contribute to reusable approaches and best practices across GSDS.

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.