Quantitative Analytics Senior Manager -Agentic AI Platform & Product Owner

Wells Fargo Bank•Charlotte, NC

2d•$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 team develops and productionizes methodologies, AI agents, and systems to transform and accelerate model development and validation at enterprise scale. In collaboration with Model Risk Officers, it integrates these capabilities into the broader risk management strategy to support consistency, reliability, and continuous improvement in model validation and modeling standards. The Senior Manager will lead and build the Enterprise Model Validation platform, AI agents, and team. The role drives automated model validation and monitoring and develops strategic partnerships with business lines, technology teams, and model risk management. It reports to the Head of Frontier AI Model Methodology. The position combines product ownership, technical leadership, and people management. The Senior Manager will oversee agentic AI product requirements and lifecycle, lead the design and delivery of cloud-native solutions, and work hands-on with teams to develop scalable platforms for real-time model validation, testing, and monitoring. The position offers a hybrid work schedule, requires willingness to work onsite at a stated job-posting location, and is eligible for visa sponsorship.

Requirements

At least 7 years of quantitative analytical experience, or equivalent demonstrated through work experience, training, military experience, education, or a combination.
At least 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

Hands-on technical leadership building agentic AI systems using generative AI, including large language and reasoning models.
Ability to apply generative AI and agentic AI across the full build lifecycle—design, implementation, testing, and iteration—using AI coding copilots such as GitHub Copilot and agentic coding workflows while maintaining enterprise standards.
Experience evaluating, validating, or monitoring AI/ML models, including agentic AI, generative AI, and machine learning models.
Product development experience, including creating product roadmaps and go-to-market plans.
Track record of delivering in a fast-paced environment and leading high-performing teams.
Experience leading teams in Agile development environments such as Scrum or Kanban, including sprint planning, delivery oversight, and continuous improvement.
Excellent leadership and communication skills and ability to build effective stakeholder partnerships.
Financial-services domain expertise delivering AI/ML and emerging AI pilots and aligning product value with platform roadmaps.
Strong understanding of current and emerging technologies, including agentic AI, for next-generation model validation systems.
Ability to build effective partnerships with model developers, validators, users, audit, and governance partners.
Experience creating a culture of continuous improvement in team efficiency and effectiveness.
Prior experience in financial services or a related industry.

Responsibilities

Drive development and delivery of AI agent-based model validation, testing, and real-time monitoring capabilities.
Provide leadership and technical expertise in designing and delivering AI agentic solutions and systems in cloud-native environments such as Google Cloud Platform, supporting real-time processing and large-scale distributed systems.
Define AI product requirements and manage the agentic product lifecycle to support successful delivery.
Lead cross-functional collaboration with technology partners to accelerate delivery and strengthen execution.
Contribute hands-on to solution design, development, and execution alongside teams.
Partner with Model Risk and Model Development teams to build scalable platforms and agent-based solutions for real-time model validation, testing, and monitoring.
Develop a culture of talent development aligned with business objectives and strategy.
Manage a team of AI scientists and engineers.