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

Wells Fargo•Charlotte, NC

13d•Onsite

About The Position

Wells Fargo is seeking talent for the 2027 Quantitative Analytics Summer Internship Program, specifically for PhD candidates in Applied Computational Intelligence (ACI). This 10-week program offers PhD candidates an opportunity to apply advanced analytics, artificial intelligence, and machine learning to complex business challenges within a leading financial institution. The internship involves hands-on project experience, mentorship, technical training, and exposure to senior leaders. Interns will collaborate with experienced quantitative professionals to develop and evaluate innovative solutions supporting business strategy, risk management, and customer experience. The program encourages interns to bring fresh perspectives, explore innovative approaches, and contribute to Wells Fargo's strategic priorities, fostering both technical capabilities and business acumen in a collaborative environment. Opportunities exist in advancing AI research, developing analytical solutions, and collaborating across the organization, with potential for full-time consideration for high-performing interns.

Requirements

2+ years of work experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
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 in Computer Science, Statistics, Data Science, Econometrics, Mathematics, Engineering or related quantitative field, with an expected graduation date after December 2027.

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.