Analyst-Data Analytics (Agentic)

Role overview

Analyst-Data Analytics (Agentic) at American Express

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.

Responsibilities

• Develop hands-on Generative AI and Data Science solutions to solve complex and unstructured business problems across GCS.
• Develop and experiment with agentic AI workflows that can reason over context, leverage relevant data and tools, and support analytical and business decision-making.
• Work with business partners and other stakeholders to understand business needs, structure analytical problems and translate them into appropriate Data Science and GenAI approaches.
• Contribute to the development of reusable GenAI approaches, analytical assets and learnings that can support applications across AIM.

Required qualifications

• Minimum qualification: Bachelor's Degree

Preferred qualifications and skills

• 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.

Employment details

• Location: Gurugram [HR] [India]

Application details

• 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.
• 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.
• Ability and curiosity to rapidly learn, experiment with and apply emerging AI techniques to practical business problems.

About the employer

• 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.

Additional role details

• 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.
• 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 position is part of a highly collaborative environment, working with partners across AIM, GCS Marketing, Product, Capabilities, Technology and other teams at American Express.
• 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.
• 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, as appropriate, to support development of scalable solutions.
• Explore emerging LLMs, GenAI techniques and frameworks and assess their applicability to relevant GCS business and analytical problems.
• Define appropriate analytical measures and success metrics, evaluate results and communicate findings, limitations and recommendations clearly to technical and non-technical stakeholders.
• 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 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.
• Excellent written and oral communication skills with the ability to articulate technical concepts and analytical findings clearly.