This listing doesn't post a salary. Most similar roles pay
$134,500–$236,187.
Based on 240 similar postings.
Employer
About Wells Fargo
Wells Fargo & Company is one of the largest banks in the United States, providing banking, investment, mortgage, and consumer and commercial finance products and services nationwide. Industry: Banking & Financial Services
Wells Fargo currently has
33 open roles
on FindRole.
Listed pay typically runs
$159,000–$260,000
across 13 roles with salary data.
The 2027 Quantitative Analytics Program – Applied Computational Intelligence (ACI PhD) – Early Careers is a twelve-month development program where participants apply advanced analytics, artificial intelligence, and machine learning to complex business challenges in the financial services sector. Participants will rotate through two six-month periods to build generative AI assistants, multi-agent systems, and intelligent agents that automate workflows and provide decision support. The role involves developing large language models, implementing retrieval-augmented generation, and managing model training, evaluation, and optimization. Key technologies include Python, Go, C++, Rust, Java, Spark, and frameworks like LangChain and LangGraph. Candidates will utilize distributed GPU training, LoRA, and PEFT to improve model performance while addressing problems in risk management, customer experience, and operational efficiency through the deployment of scalable AI solutions and advanced statistical techniques for model validation.
Develop and deploy AI-powered decision support systems using large language models and multi-agent workflows.
Build Generative AI assistants that leverage enterprise knowledge for employee and customer support.
Create knowledge intelligence platforms using Retrieval-Augmented Generation (RAG) and multimodal AI.
Train, fine-tune, and optimize transformer-based foundation models and small language models.
Implement model optimization techniques such as distillation, quantization, and pruning to improve inference performance.
Monitor production models for stability, performance, and drift using advanced analytics frameworks.
Apply statistical and quantitative techniques to validate model design, calibration, and implementation.
Develop scalable machine learning pipelines using distributed computing and advanced training techniques.
What we're looking for
Must have a Master's degree or higher in statistics, mathematics, physics, engineering, computer science, economics, or a quantitative discipline.
Must have 2+ years of Quantitative Analytics experience or equivalent through work, training, or education.
Currently pursuing a PhD with an expected graduation between December 2026 and June 2027, or completed a PhD after May 2024 while completing a postdoc (preferred).
Must have strong programming experience with tools such as Python, Go, C++, Rust, Java, Spark, or similar technologies.
Must have hands-on experience developing machine learning and AI solutions in research, academic, or industry environments.
Experience with Large Language Models, model training, supervised fine-tuning, and post-training methodologies like RLHF, RLAIF, PPO, DPO, and GRPO (preferred).
Experience with agentic AI, multi-agent architectures, and orchestration frameworks like LangChain, LangGraph, or CrewAI (preferred).
Experience with Retrieval-Augmented Generation (RAG) applications and distributed GPU training/optimization techniques like LoRA and PEFT (preferred).
Python
C++
SQL
Java
Monte Carlo methods
Differential equations
Linear algebra
Applied probability
Statistics
Generative AI
Data Analysis
Modeling
Software Engineering
Data Management
Quantitative Analytics
Python
C++
SQL
Java
Machine Learning
Generative AI
Monte Carlo Methods
Linear Algebra
Statistics
Data Analysis
Modeling
Risk Management
Differential Equations
Applied Probability