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

About this role

About this role:   Wells Fargo is seeking talent to join the   2027 Quantitative Analytics Program   ACI   ( PhD ).   Learn more about the career areas and lines of business at   wellsfargojobs.com .   

Program Overview |   The Wells Fargo Quantitative Analytics Program offers PhD candidates an opportunity to apply advanced analytics, artificial intelligence, and machine learning to complex business challenges at one of the world's leading financial institutions.  

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 prioritie s . Along the way,   you'll   develop not only your technical capabilities but also the business acumen and leadership skills needed to succeed in a highly collaborative environment.  

Upon completion of the program,   you'll   transition into a full-time role aligned with your skills, interests, program   experience,   and business   needs .     #earlycareers  

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:  

• 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.  

• D eploy   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 .  

What   You’ll   Experience:  

• 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.  

Program dates:   July 2027 - July 2028      

Program Duration :   12 months      

Program Location:   Charlotte, NC       

Required Qualifications:

• 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

Required Qualifications for Europe, Middle East & Africa only: • Experience in Quantitative Analytics, or equivalent demonstrated through one or a combination of the following: work experience, training, education • Master's degree or higher in statistics, mathematics, physics, engineering, computer science, economics, or quantitative discipline

Desired Qualifications:  

• 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 .  

Technical Skills  

• Strong programming experience with tools such as Python,   G o ,   C++ , R ust,   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 Tr

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