Principal Quantitative Analytics Specialist

Wells Fargo•Irving, TX

22h•Hybrid

About The Position

About this role: Wells Fargo is seeking a Principal Quantitative Analytics Specialist to serve as the hands‑on architectural authority for reusable GenAI, agentic, and advanced analytics capabilities, operating at enterprise scale while remaining an individual contributor. Responsible for re‑architecting complex analytics and AI systems into modular, composable, reusable capabilities that materially reduce delivery cycle time and raise the engineering bar across teams. The principal does not merely advise on architecture—they design, prototype, and validate reference implementations that become the foundation for scaled delivery. This role sits at a critical inflection point: transforming analytics delivery from bespoke solutions to durable, governed platforms that balance speed, reliability, and regulatory rigor. In this role, you will: Execute Architecture & Re‑Architecture Act as a principal architect and execution lead for GenAI and agentic capabilities spanning multiple portfolios. Identify architectural bottlenecks in existing solutions and lead their redesign into modular, reusable components (agents, tools, evaluation modules, guardrails, observability layers). Design and validate reference architectures for agentic workflows, including tool use, orchestration, memory, and failure recovery patterns. Translate architectural intent into working code, templates, and patterns that teams can directly adopt. Ensure Velocity Through Reuse and Platformization Reduce build‑to‑production cycle time by standardizing abstractions, interfaces, and evaluation pipelines. Establish and evangelize “build once, reuse many” principles across GenAI and advanced analytics use cases. Introduce engineering practices that improve velocity and safety, including: Contract‑driven component interfaces Automated evaluation and regression testing Safe fallback and degradation patterns Have Hands‑On Technical Leadership Personally design, prototype, and validate agentic and GenAI architectural spikes. Review and challenge system designs and implementations produced by senior engineers and data scientists. Serve as a technical authority on architectural trade‑offs, failure modes, and system boundaries, while stopping short of formal people management. Raise organizational capability by teaching through execution, not documentation alone. Ensure Evaluation, Guardrails, and Reliability by Design Define reusable LLM and agent evaluation frameworks covering quality, hallucination, robustness, bias, cost, and latency. Ensure evaluation, monitoring, and failure analysis are first‑class architectural components. Anticipate system failure modes and design solutions that fail safely and observably in regulated environments. Support Governance, Risk, and Partnership Partner closely with Technology, Product, and Model Risk to ensure architectures are: Explainable Auditable Scalable under regulatory scrutiny Convert architectural decisions into clear, reusable artifacts that support validation, audit, and reuse. Balance rapid execution with the realities of operating in a highly regulated financial environment.

Requirements

10+ 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

8+ years of experience designing and delivering advanced analytics, ML, or AI systems.
Demonstrated experience re‑architecting existing systems for modularity, reuse, and speed.
Advanced proficiency in Python and modern AI/ML engineering practices.
Proven ability to operate as a hands‑on architectural authority without formal people management.
Experience designing or contributing to internal AI platforms or developer frameworks.
Deep exposure to agentic AI systems, LLM evaluation, and reliability engineering.
Experience operating in regulated industries.
Prior exposure to hyperscale or hyperscale‑adjacent engineering practices (strong plus)

Responsibilities

Execute Architecture & Re‑Architecture Act as a principal architect and execution lead for GenAI and agentic capabilities spanning multiple portfolios.
Identify architectural bottlenecks in existing solutions and lead their redesign into modular, reusable components (agents, tools, evaluation modules, guardrails, observability layers).
Design and validate reference architectures for agentic workflows, including tool use, orchestration, memory, and failure recovery patterns.
Translate architectural intent into working code, templates, and patterns that teams can directly adopt.
Ensure Velocity Through Reuse and Platformization Reduce build‑to‑production cycle time by standardizing abstractions, interfaces, and evaluation pipelines.
Establish and evangelize “build once, reuse many” principles across GenAI and advanced analytics use cases.
Introduce engineering practices that improve velocity and safety, including: Contract‑driven component interfaces Automated evaluation and regression testing Safe fallback and degradation patterns
Have Hands‑On Technical Leadership Personally design, prototype, and validate agentic and GenAI architectural spikes.
Review and challenge system designs and implementations produced by senior engineers and data scientists.
Serve as a technical authority on architectural trade‑offs, failure modes, and system boundaries, while stopping short of formal people management.
Raise organizational capability by teaching through execution, not documentation alone.
Ensure Evaluation, Guardrails, and Reliability by Design Define reusable LLM and agent evaluation frameworks covering quality, hallucination, robustness, bias, cost, and latency.
Ensure evaluation, monitoring, and failure analysis are first‑class architectural components.
Anticipate system failure modes and design solutions that fail safely and observably in regulated environments.
Support Governance, Risk, and Partnership Partner closely with Technology, Product, and Model Risk to ensure architectures are: Explainable Auditable Scalable under regulatory scrutiny Convert architectural decisions into clear, reusable artifacts that support validation, audit, and reuse.
Balance rapid execution with the realities of operating in a highly regulated financial environment.