Analyst-Data Analytics (Agentic)

About the Role

The Analytics, Investment Optimization and Marketing Enablement (AIM) team, part of the Global Commercial Services Marketing group at American Express, is the analytical engine for the Global Commercial Card & Non-Card business. The team drives profitable business growth through data, analytics, decision science, and AI-powered capabilities, providing actionable insights and innovative solutions. This role, based in Gurugram, focuses on developing and applying advanced Data Science and Generative AI solutions across Global Commercial Services. The work spans customer acquisition, engagement, targeting, personalization, product and portfolio analytics, marketing measurement, and analytical productivity. A key focus is applying Generative AI to business and analytical problems, translating opportunities into hands-on solutions and prototypes. The role involves applying Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), prompt engineering, agentic AI, and other emerging AI techniques alongside traditional Data Science and Machine Learning. The position is part of a highly collaborative environment, working with partners across AIM, GCS Marketing, Product, Capabilities, Technology, and other teams.

Key Responsibilities

• Develop hands-on Generative AI and Data Science solutions to solve complex and unstructured business problems across GCS.
• Design, build, and experiment with LLM-powered solutions using techniques such as prompt engineering, Retrieval-Augmented Generation (RAG), embeddings, semantic search, and vector-based retrieval.
• Develop and experiment with agentic AI workflows that can reason over context, leverage relevant data and tools, and support analytical and business decision-making.
• Develop and apply evaluation approaches for GenAI solutions across dimensions such as response quality, relevance, consistency, and business effectiveness, with appropriate consideration for responsible AI, controls, and governance.
• Apply Machine Learning and statistical techniques alongside Generative AI to develop fit-for-purpose solutions for customer acquisition, engagement, targeting, personalization, measurement, and other business opportunities.
• Explore structured and unstructured data from multiple sources to identify relevant signals and develop innovative approaches that transform data into actionable insights and AI-powered solutions.
• Prototype, test, and refine emerging GenAI techniques and collaborate with Technology, Capabilities, and other partner teams to support the development of scalable solutions.
• Explore emerging LLMs, GenAI techniques, and frameworks and assess their applicability to relevant GCS business and analytical problems.
• Work with business partners and other stakeholders to understand business needs, structure analytical problems, and translate them into appropriate Data Science and GenAI approaches.
• Define appropriate analytical measures and success metrics, evaluate results, and communicate findings, limitations, and recommendations clearly to technical and non-technical stakeholders.
• Contribute to the development of reusable GenAI approaches, analytical assets, and learnings that can support applications across AIM.
• Demonstrate curiosity and willingness to challenge the status quo; bring innovative thinking to generate insights, alternatives, and opportunities for business success.

Shift Time: 1:30 PM - 9:30 PM IST

Requirements & Tech Stack

• Strong programming skills in Python.
• Working knowledge of SQL.
• Hands-on understanding of Large Language Models (LLMs) and core GenAI techniques such as prompt engineering and Retrieval-Augmented Generation (RAG).
• Understanding of 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.
• Ability to rapidly learn, experiment with, and apply emerging AI techniques to practical business problems.

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 foundation in Data Science and Machine Learning concepts, including statistical modeling, supervised and unsupervised learning, model evaluation, and experimentation.
• Strong analytical/conceptual thinking acumen with the ability to structure and solve unstructured and complex 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.