Quantitative Analytics Senior Manager -Agentic AI Platform & Product Owner

Wells Fargo•St. Louis, MO

7d•$185,000 - $300,000•Hybrid

About The Position

Wells Fargo is seeking an experienced Quantitative Analytics Senior Manager to join the Frontier AI Model Methodology team. The Frontier AI Model Methodology team plays a critical role in developing and productionizing methodologies, AI agents, and systems that transform and accelerate model development and validation at Wells Fargo scale. One of its key functions is to integrate, in close collaboration with Model Risk Officers, these advanced methodologies and AI agents into the overall risk management strategy, ensuring consistency and alignment with enterprise-wide objectives. This synergy enhances the robustness and reliability of models leveraged by the company, strengthens the effectiveness of model validations, and contributes to the continuous improvement of modeling standards and practices across the enterprise. In this role you will manage, lead and build our Enterprise Model Validation platform, AI agents, and team. You will be responsible for driving automated validation and monitoring of models, while building strategic partnerships with stakeholders including Lines of businesses, technology and model risk management. This role will report to the Head of Frontier AI Model Methodology.

Requirements

7+ years of Quantitative Analytical experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
3+ years of management or leadership experience
Master's degree or higher in a quantitative discipline such as mathematics, statistics, engineering, physics, economics, or computer science

Nice To Haves

Prior experience as a hands-on technical leader building Agentic AI systems leveraging GenAI (including Large Language and Reasoning Models)
Demonstrated ability to apply GenAI / Agentic AI in the full build lifecycle - design, implementation, testing, and iteration - leveraging AI coding copilots (e.g., GitHub Copilot) and agentic coding workflows to accelerate delivery while maintaining enterprise standards
Prior experience in AI / ML (including Agentic AI, GenAI, ML) model evaluation, validation, real-time model monitoring
Experience in Product Development, including developing product roadmaps and go to market plans
Proven track record in delivering in fast-past environment
Proven track record in leading high performing teams
Proven experience leading and managing teams in Agile development environments (e.g., Scrum, Kanban), including sprint planning, delivery oversight, and continuous improvement.
Excellent leadership and communication skills with the ability to build effective partnerships with stakeholders
Strong domain expertise in financial services in delivering AI/ML and emerging AI pilots and aligning product, product value, and platform roadmaps.
Strong understanding of current and emerging technology landscape (including Agentic AI) supporting the delivery of new generation of model validation system
Ability to build excellent partnerships with model developers, validators, users, audit, and governance partners
Success in creating a culture of continuous improvement in efficiency and effectiveness within the team
Prior experience in financial services or related industry.

Responsibilities

Drive the development and delivery of industry-leading, AI agentic-based model validation, testing, and real-time monitoring capabilities
Provide leadership and technical expertise in designing and delivering AI agentic solutions and systems on cloud-native environment such as Google Cloud Platform (GCP), enabling real‑time processing and large‑scale distributed systems.
Define AI Product requirements and manage the agentic product lifecycle to ensure successful delivery.
Lead cross‑functional collaboration with Technology partners to unlock synergies that accelerate delivery and strengthen execution.
Demonstrate hands‑on leadership by directly contributing to design, development, and execution of solutions alongside teams.
Partner with the Model Risk and Model Development teams to design and build scalable platforms and agent-based solutions that accelerate real-time model validation, testing and monitoring.
Develop and guide a culture of talent development to meet business objectives and strategy.
Manage a team of highly skilled AI scientists and engineers.