Generative AI Data Scientist — Wealth Analytics

This is an opportunity to build and ship Generative AI models that power a live, intelligent client engagement platform at the heart of Citi's Wealth business. As a Generative AI Data Scientist within Citi's Analytics & Information Management function, you will design, train, and deploy LLM-based solutions that help financial advisors reach the right clients, with the right message, at the right time — at scale and with measurable impact. You will go beyond modelling to own the analytics that measure how the platform performs and inform how it improves.

Design and implement Generative AI and LLM-based solutions using Python, PySpark, and SQL, applying techniques including prompt engineering, Retrieval Augmented Generation (RAG), and model fine-tuning to power an intelligent client engagement platform. Train, validate, and deploy machine learning models across the full model lifecycle — from data extraction and exploratory analysis through to production deployment and ongoing governance. Analyse structured and unstructured datasets to surface business insights that directly inform product decisions and strategic priorities within the Wealth business. Harden GenAI components for production use by improving output relevance and quality, building in monitoring and evaluation, and ensuring models meet Citi's compliance and governance standards. Build and apply product analytics to measure platform adoption, quantify value delivered to advisors, and identify where users encounter friction — then translate those findings into concrete platform improvements. Collaborate with product managers, engineers, and business stakeholders to move solutions from prototype to production, integrating models directly into platform workflows. Evaluate emerging AI and LLM capabilities for applicability to the platform, and communicate findings clearly through presentations and analytical reports to technical and non-technical audiences. Required qualifications & skills 1–3 years of hands-on experience in an AI, machine learning, or data science role, with direct exposure to deep learning or generative AI applications. Proficiency in Python and SQL, including writing efficient queries, processing large datasets, and applying these tools to build and validate analytical solutions. Practical experience working with Large Language Models (such as OpenAI, Claude, or Gemini) and frameworks including TensorFlow, PyTorch, Hugging Face Transformers, LangChain, or LlamaIndex. Ability to apply core machine learning techniques — including supervised and unsupervised modelling, statistical analysis, hypothesis testing, and data visualisation — to generate clear, actionable insights. Familiarity with vector databases and model fine-tuning techniques, and the ability to validate and maintain models once deployed in production. Bachelor’s degree in computer science, Data Science, Statistics, Mathematics, Engineering, Economics, or a related quantitative discipline. Beneficial skills & qualifications Exposure to product development practices such as working with engineering teams, version control, API integration, or feature monitoring within a live platform or application. Experience using product analytics to define metrics, measure user adoption and engagement, and analyse behavioural patterns such as drop-offs or conversion funnels. Awareness of what makes an AI platform production-grade, including evaluation frameworks, guardrails, observability, and scalability considerations. Published research, conference presentations, or certifications in AI, machine learning, data science, or analytics. Domain experience in Wealth Management, Credit Cards, or Retail Banking.

Joining this team means working on a production-grade GenAI platform that is already delivering measurable impact for Citi's Wealth business — not a proof of concept, but a live system used by financial advisors at scale. You will work within one of