Senior AI Lead Data Management Analyst

Wells Fargo is hiring a Senior AI Lead Data Management Analyst (internally, Senior Lead AI & Risk Analytics Analyst) to design and operationalize AI-enabled analytical solutions that strengthen how the enterprise identifies, monitors, and mitigates emerging risk. The role combines risk analytics, prompt engineering, and data visualization to turn complex business and risk data into actionable intelligence for business, risk, technology, and governance partners.

You will build production AI applications and agentic workflows using large language models, retrieval-augmented generation (RAG), and agentic frameworks to support risk identification, thematic reviews, and executive reporting. A central part of the work is governance: developing prompt libraries, governance standards, testing methodologies, and reusable AI assets, and evaluating AI outputs for accuracy, explainability, consistency, and adherence to enterprise risk and AI governance requirements. You will partner with compliance and risk management teams to ensure AI solutions meet regulatory and governance obligations in a highly regulated environment.

The role also leads emerging-risk detection across large, complex datasets, defines key risk indicators (KRIs), metrics, and thresholds, and builds executive dashboards and drill-through analytics that surface trends, concentrations, root causes, and control weaknesses. It serves as a strategic advisor on AI adoption, model risk, and analytical maturity across Risk, Data Management, Technology, Compliance, and Audit.

Qualifications

• 7+ years of Data Management, Business Analysis, Analytics, or Project Management experience (or equivalent through work, training, military, or education)
• 3+ years of AI prompt engineering, AI agent development, advanced risk analytics, and executive reporting, translating complex risk data into AI-enabled insights and decision support
• Understanding of AI security, responsible AI principles, model risk management, data privacy, explainability, and regulatory compliance within highly regulated environments
• Knowledge of AI/LLMOps practices: model evaluation, prompt optimization, version control, observability, performance monitoring, and governance controls
• Hands-on experience deploying AI/LLM applications in production, including RAG with vector databases, and AI orchestration frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel)
• Experience designing or evolving enterprise data governance operating models, with familiarity in data quality metrics, data lineage, and issue tracking
• Strong proficiency in Python and SQL; experience with Azure, AWS, or Google Cloud AI services