2027 Quantitative Analytics Summer Internship Capital Markets (PhD) – Early Careers

Wells Fargo Bank•Charlotte, NC

16h•Onsite

About The Position

Wells Fargo is seeking talent to join the 2027 Quantitative Analytics Summer Internship Program Capital Markets (PhD). The Wells Fargo Quantitative Analytics Internship 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 10-week summer internship combines hands-on project experience, mentorship, technical training, and exposure to senior leaders. Through this 10-week internship, interns will work alongside experienced quantitative professionals, helping develop and evaluate innovative solutions that support business strategy, risk management, and customer experience across Wells Fargo. Interns are expected to bring fresh perspectives, explore innovative approaches, and contribute to solutions that support Wells Fargo's strategic priorities. Along the way, interns will develop not only their technical capabilities but also the business acumen and leadership skills needed to succeed in a highly collaborative environment. High performing interns may receive consideration for full-time roles after graduation. Interns could work on high-impact projects like: Developing pricing models that are used by various Wells Fargo trading desks, Enhancing production models to mitigate any deterioration in model performance, Developing simulation models to forecast losses for trading portfolios, Developing benchmark models to assess potential limitations of production models, Providing analysis and reporting of metrics utilized to assess ongoing model performance, Applying statistical and quantitative techniques to validate model design, calibration, and implementation.

Requirements

2+ years of work experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education.
Currently pursuing a PhD degree in Mathematics, Physics, Engineering, Statistics or related quantitative field, with an expected graduation date after December 2027.
Excellent programming skills for data and statistical analysis such as Python, C++, SQL, and Java.
Experience and demonstrated knowledge in mathematical and numerical methods including Monte Carlo methods, differential equations, linear algebra, applied probability, and statistics.
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 and understanding of capital markets, 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.

Responsibilities

Apply advanced analytics, artificial intelligence, and machine learning to complex business challenges.
Develop and evaluate innovative solutions that support business strategy, risk management, and customer experience.
Bring fresh perspectives and explore innovative approaches.
Contribute to solutions that support Wells Fargo's strategic priorities.
Develop technical capabilities, business acumen, and leadership skills.
Develop pricing models.
Enhance production models.
Develop simulation models.
Develop benchmark models.
Provide analysis and reporting of metrics.
Apply statistical and quantitative techniques to validate model design, calibration, and implementation.