About this role: Wells Fargo is seeking talent to join the 2027 Quantitative Analytics Program RADS (Masters). 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'llwork alongside experienced quantitative professionals, helping develop and evaluate innovative solutions that support business strategy, risk management, and customer experience across Wells Fargo.
You'llbeexpectedto bring fresh perspectives, explore innovative approaches, and contribute to solutions that support Wells Fargo's strategic priorities. Along the way,you'lldevelop 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'lltransition into a full-time role aligned with your skills, interests, program experience, and business needs. #earlycareers
You could work on high-impact projects like:
Forecasting loss and revenue for credit card loan portfolios
Developing credit scorecards for consumer decisioning strategies
Building models to identify money laundering patterns across massive transaction databases
Predicting operational losses using statistical and machine learning modeling
Applying statistical and quantitative techniques to validate model design, calibration, and implementation.
What You’ll Experience:
Step into a data-driven environment where advanced analytics, machine learning, and emerging technologies power smarter decisions across the enterprise. In the Quantitative Analytics Program, you’ll work at the intersection of data, risk, and innovation—solving complex problems that directly impact customers and the broader financial system.
You’ll design and deploy models that inform critical decisions across credit risk, financial crime, customer experience, and operations. From forecasting portfolio performance to applying generative AI in underwriting and customer interactions, your work will drive meaningful outcomes at scale.
Working with large, real-world datasets, you’ll partner with cross-functional teams and senior leaders to turn sophisticated quantitative techniques into actionable insights. Whether detecting fraud, optimizing strategies, or building AI-driven solutions, you’ll play a key role in shaping how data powers the future of banking.
Program dates:July 2027 - July 2028
Program Duration:12 months
Program Location:Charlotte, NC
Required Qualifications:
Required Qualifications for Europe, Middle East & Africa only:
Desired Qualifications:
Currently pursuing a Masters degree in Statistics, Data Science, Mathematics, Econometrics, Computer Science, Engineering or related quantitative field, with an expected graduation date between December 2026 – June 2027
Excellent programing skills and use of statistical software packages such as Python, R, SQL, Spark and Java
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.
Experience and demonstrated first-hand knowledge in a number of these areas: machine learning/AI models data analysis, statistical modeling, data management, and computing
Join us at Wells Fargo and become a part of a dynamic team that is shaping the future of the financial industry. Apply now and embark on a journey towards personal and professional growth in quantitative analytics.
Wells Fargo only considers candidates who are presently authorized to work for any employer in the United States and who do not require work visa sponsorship from Wells Fargo now or in the futurein order toretaintheir authorization to work in the United States.
Based on the volume of applications received, this job posting may be removed prior to the indicated close date. If you do not apply prior to the closing of thisposting, we encourage you to apply for other opportunities with Wells Fargo. Aftersubmittingyour application, pleasemonitoryour e-mail for future communications.
Posting End Date:
21 Sep 2027*Job posting may come down early due to volume of applicants.
We Value Equal Opportunity
Wells Fargo is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other legally protected characteristic.
Employees support our focus on building strong customer relationships balanced with a strong risk mitigating and compliance-driven culture which firmly establishes those disciplines as critical to the success of our customers and company. They are accountable for execution of all applicable risk programs (Credit, Market, Financial Crimes, Operational, Regulatory Compliance), which includes effectively following and adhering to applicable Wells Fargo policies and procedures, appropriately fulfilling risk and compliance obligations, timely and effective escalation and remediation of issues, and making sound risk decisions. There is emphasis on proactive monitoring, governance, risk identification and escalation, as well as making sound risk decisions commensurate with the business unit’s risk appetite and all risk and compliance program requirements.
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