Analyst-Data Analytics (Agentic)

About the Role

The Analytics, Investment Optimization and Marketing Enablement (AIM) team, part of Global Commercial Services Marketing at American Express, serves as 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, delivering actionable insights and innovative solutions to shape business and marketing strategy. This role, based in Gurugram, focuses on developing and applying advanced Data Science and Generative AI solutions within Global Commercial Services, tackling high-impact business problems.

Key Responsibilities

• Develop hands-on Generative AI and Data Science solutions for complex business problems.
• Design, build, and experiment with LLM-powered solutions using techniques like prompt engineering, Retrieval-Augmented Generation (RAG), embeddings, semantic search, and vector-based retrieval.
• Develop and experiment with agentic AI workflows for reasoning, tool utilization, and decision-making support.
• Develop and apply evaluation approaches for GenAI solutions focusing on quality, relevance, consistency, and business effectiveness, including considerations for responsible AI and governance.
• Apply Machine Learning and statistical techniques alongside Generative AI for customer acquisition, engagement, targeting, personalization, measurement, and other business opportunities.
• Explore structured and unstructured data to identify signals and develop innovative, actionable insights and AI-powered solutions.
• Prototype, test, and refine emerging GenAI techniques, collaborating with Technology, Capabilities, and partner teams for scalable solutions.
• Explore emerging LLMs, GenAI techniques, and frameworks, assessing their applicability to business and analytical problems.
• Collaborate with business partners to understand needs, structure analytical problems, and translate them into Data Science and GenAI approaches.
• Define analytical measures and success metrics, evaluate results, and communicate findings and recommendations to stakeholders.
• Contribute to the development of reusable GenAI approaches, analytical assets, and learnings.
• Demonstrate curiosity, innovate, and generate opportunities for business success.

Requirements & Tech Stack

• Strong programming skills in Python.
• Working knowledge of SQL.
• 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 (LLMs) and core GenAI techniques like prompt engineering and Retrieval-Augmented Generation (RAG), including concepts like embeddings and semantic retrieval.
• Understanding of approaches for evaluating GenAI solutions (response quality, relevance, consistency, reliability).
• Strong analytical/conceptual thinking acumen for structuring and solving complex business problems.
• Ability and curiosity to rapidly learn, experiment with, and apply emerging AI techniques.
• Excellent written and oral communication skills.
• Shift Time: 1:30 PM - 9:30 PM IST

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.
• Demonstrated hands-on experience developing Generative AI solutions through professional work, internships, academic research, or substantial projects.

Preferred Qualifications

• Hands-on exposure to agentic AI concepts and workflows (tool use, reasoning, 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 model selection considerations.
• Exposure to advanced GenAI techniques like model fine-tuning or parameter-efficient fine-tuning.
• Experience working with unstructured data or Natural Language Processing (NLP) techniques.
• Familiarity with responsible AI principles and considerations in enterprise environments.
• Interest in applying AI and GenAI capabilities to marketing, customer engagement, personalization, analytical productivity, and business decision-making.