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

Job description

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.

Responsibilities

Decision Science & Modeling Develop analytical solutions, predictive models, recommendation and decisioning approaches that improve servicing experiences, personalization, colleague effectiveness, and operational outcomes. Explore and analyse large customer, behavioural, interaction, and operational datasets to identify opportunities, engineer features, test hypotheses, and translate findings into actionable solutions.

AI / GenAI & Experimentation Build and evaluate machine learning and GenAI solutions using appropriate modeling, experimentation, and validation techniques. 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. Design and analyze experiments and performance measurements to assess model/AI quality and business impact and identify opportunities for continuous improvement.

Implementation & Partnership Write high-quality analytical code and work with data, Technology, and platform partners to support productionization, monitoring, and ongoing enhancement of Decision Science solutions. Develop strong cross-functional relationships, communicate analytical findings clearly, and help translate technical results into recommendations that drive action. Innovation & Technical Growth Stay current on advances in machine learning, GenAI, recommendation systems, and decision intelligence; test relevant methods and contribute reusable approaches and best practices across GSDS.

Qualifications

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.