Analyst-Data Analytics (Agentic)

The Analytics, Investment Optimization and Marketing Enablement (AIM) team – a part of Global Commercial Services Marketing group within American Express – is the analytical engine that enables the Global Commercial Card & Non-Card business. The team enables profitable business growth through data, analytics, decision science and AI-powered capabilities, providing actionable insights and innovative solutions to drive business and marketing strategy.

This role, based out of Gurugram, will be a part of the AIM team and will focus on developing and applying advanced Data Science and Generative AI solutions across Global Commercial Services. The incumbent will work on high-impact business problems spanning customer acquisition, engagement, targeting and personalization, product and portfolio analytics, marketing measurement and analytical productivity.

The role will focus on applying Generative AI to business and analytical problems and translating opportunities into hands-on solutions and prototypes. The incumbent will apply Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), prompt engineering, agentic AI and other emerging AI techniques alongside traditional Data Science and Machine Learning approaches.

The position is part of a highly collaborative environment, working with partners across AIM, GCS Marketing, Product, Capabilities, Technology and other teams at American Express.

Qualifications

- Bachelor’s or Master’s degree in a quantitative or technical field such as Computer Science, Engineering, Mathematics, Statistics, Economics, Data Science, Artificial Intelligence or a related discipline.
- 0–3 years of relevant experience in Data Science, Machine Learning, Generative AI or Analytics, with demonstrated hands-on experience developing Generative AI solutions through professional work, internships, academic research or substantial projects.
- Strong programming skills in Python and working knowledge of SQL, with the ability to manipulate data, conduct analysis and develop Data Science/AI solutions.
- Strong foundation in Data Science and Machine Learning concepts, including statistical modeling, supervised and unsupervised learning, model evaluation and experimentation.
- Hands-on understanding of Large Language Models and core GenAI techniques such as prompt engineering and Retrieval-Augmented Generation (RAG), including concepts such as embeddings, semantic retrieval and using relevant data and context to improve LLM responses.
- Understanding of approaches for evaluating GenAI solutions across dimensions such as response quality, relevance, consistency and reliability.
- Strong analytical/conceptual thinking acumen with the ability to structure and solve unstructured and complex business problems.
- Ability and curiosity to rapidly learn, experiment with and apply emerging AI techniques to practical business problems.
- Excellent written and oral communication skills with the ability to articulate technical concepts and analytical findings clearly.

Preferred Qualifications

- Hands-on exposure to agentic AI concepts and workflows involving capabilities such as tool use, reasoning or multi-step orchestration.
- Experience with GenAI/LLM frameworks and libraries such as LangChain, LangGraph, LlamaIndex, Hugging Face or equivalent tools.
- Experience with vector-based retrieval, semantic search, advanced RAG techniques or LLM evaluation frameworks.
- Familiarity with commercial and open-source LLMs and considerations involved in selecting appropriate models and approaches for different use cases.
- Exposure to advanced GenAI techniques such as model fine-tuning, parameter-efficient fine-tuning or advanced retrieval approaches.
- Experience working with unstructured data or Natural Language Processing techniques.
- Familiarity with responsible AI principles and considerations related to applying AI within enterprise or regulated environments.
- Interest in applying AI and GenAI capabilities to marketing, customer engagement, personalization, analytical productivity and business decision-making.