About this role:Wells Fargo is seeking talent to join the2027 Quantitative Analytics Program Capital Markets (PhD). Learn more about the career areas and lines of business at. 

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 priorities. 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 one of the quant teams aligned with your skills, interests, program experience, and business needs. #earlycareers 

You could work on high-impact projects like:  

• Developing pricing and risk 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. 

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 aPhD degree with an expected graduation date in December 2026-June 2027 OR graduated from a PhD program after May 2024 and are currently completing a postdoc with emphasis in Mathematics, Physics, Engineering, Statistics or related quantitative field.    

• Excellent programing skills and use of statistical software packages such as Python, C++,SQL,and Java.

• Experience anddemonstratedknowledge 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 communicationskills, 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.