Lead Quantitative Analytics Specialist - Agentic AI Systems & Platforms

Wells Fargo & Company•Irving, TX

1d•$159,000 - $305,000•Hybrid

About The Position

Wells Fargo is seeking an experienced Lead Quantitative Analytics Specialist 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 be a hands‑on technical leader, architect, and builder, responsible for designing, developing, and delivering our Enterprise Model Validation platform, and AI agents. Success is defined by your ability to build production‑grade systems, influence through technical leadership, and partner closely with model developers, validators, and technology teams.

Requirements

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

Nice To Haves

Prior experience building Agentic AI systems leveraging GenAI (including Large Language and Reasoning Models)
Strong hands‑on software development experience, with proficiency in Python and experience building production‑grade platforms.
Experience leveraging AI coding copilots and agentic development workflows (e.g., GitHub Copilot) to accelerate delivery while maintaining enterprise standards.
Experience building or operating model evaluation, validation, and real‑time monitoring solutions, governed by AI agents.
Proven ability to design, build, test, deploy, and iterate end‑to‑end solutions.
Experience designing and operating cloud‑native solutions such as GCP, including real‑time or distributed systems.
Experience working in financial services or regulated environments, with exposure to model risk, governance, or audit expectations.
Strong ability to collaborate with model developers, validators, technology, and governance partners.
Ability to deliver in fast‑paced, ambiguous environments, balancing rigor and speed.
Recognized technical leader (Individual Contributor) with strong communication skills and a track record of improving engineering quality and efficiency.

Responsibilities

Design, build, and deliver industry‑leading Enterprise Model Validation platform, and AI agents for model testing and real‑time monitoring platforms, with a strong emphasis on production readiness and scalability.
Architect and implement AI agentic solutions and systems on cloud-native environment such as Google Cloud Platform (GCP), enabling real‑time processing and large‑scale distributed systems.
Act as a hands‑on senior engineer, directly contributing code across the stack (design, development, testing, deployment, and iteration).
Provide technical leadership through architecture, design reviews, and implementation, setting a high engineering bar by example.
Partner closely with Technology teams to unlock synergies that accelerate delivery, integration, and operationalization.
Collaborate with Model Risk Management and Model Development teams to design and build scalable platforms and AI agent‑based solutions for real‑time model testing and monitoring.
Translate complex model risk, validation, and governance requirements into robust, automated, and extensible technical solutions, governed by AI agents.
Influence platform direction by contributing to technical roadmaps, design decisions, and prioritization, without owning formal product or people management.
Champion engineering best practices, including reliability, observability, security, and maintainability, across AI and ML platforms.