2027 Quantitative Analytics Program – Applied Computational Intelligence (ACI PhD) – Early Careers

Wells Fargo•Charlotte, NC

16h•Onsite

About The Position

Wells Fargo is seeking talent to join the 2027 Quantitative Analytics Program ACI (PhD). This 12-month development program combines hands-on project experience, mentorship, technical training, and exposure to senior leaders. Through two six-month rotations, you'll work alongside experienced quantitative professionals, helping develop and evaluate innovative solutions that support business strategy, risk management, and customer experience across Wells Fargo. You'll be expected to bring fresh perspectives, explore innovative approaches, and contribute to solutions that support Wells Fargo's strategic priorities. Upon completion of the program, you'll transition into a full-time role aligned with your skills, interests, program experience, and business needs. In this opportunity you will bring deep research expertise into a real-world enterprise environment, where cutting-edge models, intelligent agents, and AI-driven decision systems can help shape the future of banking. You will contribute to high-impact projects involving large language models, multi-agent workflows, retrieval-augmented generation, human-in-the-loop AI, model evaluation, automation, and responsible AI deployment at enterprise scale. As part of the Applied Computational Intelligence track, projects may include developing AI-powered advisors and decision support systems, building Generative AI assistants and intelligent agents, designing and deploying agentic AI and multi-agent systems, creating enterprise knowledge intelligence platforms, advancing the state of enterprise AI through model training, evaluation, optimization, and deployment, and deploying scalable Generative AI and machine learning solutions. You will also apply statistical and quantitative techniques to validate model design, calibration, and implementation.

Requirements

2+ 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 statistics, mathematics, physics, engineering, computer science, economics, or quantitative discipline
Experience in Quantitative Analytics, or equivalent demonstrated through one or a combination of the following: work experience, training, education (for Europe, Middle East & Africa only)
Master's degree or higher in statistics, mathematics, physics, engineering, computer science, economics, or quantitative discipline (for Europe, Middle East & Africa only)
Strong programming experience with tools such as Python, Go, C++, Rust, Java, Spark, or similar technologies
Hands-on experience developing machine learning and AI solutions in research, academic, or industry environments
Knowledge and Experience In: Large Language Models & Model Training
Experience with supervised fine-tuning (SFT) and post-training methodologies including RLHF, RLAIF, PPO, DPO, and GRPO
Training and deploying models in cloud environments, including GCP
Agentic AI
Multi-agent architectures and orchestration frameworks like LangChain, LangGraph, Google's ADK, CrewAI
Retrieval-Augmented Generation (RAG) applications and intelligent agent deployment
AI Infrastructure & Optimization
Distributed GPU training
Efficient model tuning approaches such as LoRA and PEFT
Strong quantitative and analytical skills, with the ability to apply data analysis, modeling, visualization, statistics, research, and generative AI to generate insights, adapt quickly, and support innovative solutions.
Ability to execute with urgency, apply data and software engineering skills to design, develop, and deliver scalable solutions, and drive operational excellence with strong data management and an enterprise mindset.
Strong communication skills, with the ability to foster an inclusive environment and actively seek, apply, and respond to feedback in collaborative analytical settings.
Strong business acumen with a commitment to providing excellent service and supporting data-informed business outcomes.
Ability to act with integrity, support risk assessments, and apply risk controls to help manage risk in a disciplined, data-driven environment.

Nice To Haves

Currently pursuing a PhD degree with an expected graduation date between December 2026 – June 2027 OR graduated from a PhD program after May 2024 and are currently completing a postdoc with emphasis in Computer Science, Machine Learning, Artificial Intelligence, Engineering or related quantitative field.

Responsibilities

Develop AI-powered advisors and decision support systems that synthesize customer, relationship, market, and enterprise data to generate insights, recommendations, and actions.
Build Generative AI assistants and intelligent agents that leverage enterprise knowledge, reasoning, and workflow orchestration to support employees and customers.
Design and deploy agentic AI and multi-agent systems that automate customer service, operational, and business processes through planning, task execution, and human-in-the-loop collaboration.
Create enterprise knowledge intelligence platforms using Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), multimodal AI, and structured and unstructured data to power search, reasoning, decision support, and workflow automation.
Advance the state of enterprise AI through model training, evaluation, optimization, and deployment of LLMs, speech technologies, and emerging foundation models.
Deploy scalable Generative AI and machine learning solutions that improve productivity, customer experience, risk management, decision-making, and operational efficiency across the enterprise.
Apply statistical and quantitative techniques to validate model design, calibration, and implementation.
Develop and deploy AI and machine learning solutions across generative AI, agentic systems, and traditional machine learning applications.
Design and build LLM-powered agents and multi-agent systems capable of planning, reasoning, task orchestration, and human-in-the-loop collaboration.
Monitor production models and AI systems, evaluating performance, stability, and model drift through testing and analytics frameworks.
Collaborate with cross-functional teams, technical experts, and business leaders across the organization.
Gain exposure to enterprise-scale AI development, governance, and risk management practices.
Design, train, fine-tune, and evaluate transformer-based, foundation, and small language models (SLMs).
Apply AI, machine learning, and generative AI techniques to solve complex business problems.
Build and optimize scalable model training and deployment pipelines.
Leverage distributed computing and advanced training techniques to improve model performance and efficiency.
Enhance model performance through distillation, quantization, and pruning.
Optimize inference speed, latency, throughout, and cost for production of AI systems.