Quantitative Analytics Program Risk Analytics and Decision Science PhD Early Careers
Wells Fargo is a financial services company providing banking, lending, credit card, investment, wealth management, commercial banking, and corporate and investment banking services to consumers, businesses, and communities.
About Wells Fargo
Wells Fargo offers checking and savings accounts, credit cards, home and personal loans, auto loans, digital banking, payments, investment and wealth management, commercial banking, and corporate and investment banking. Its services support personal customers, small businesses, commercial clients, and institutional customers, with additional customer support through branches, ATMs, online banking, and the Wells Fargo Mobile app.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will apply advanced analytics, artificial intelligence, and machine learning to complex business challenges through a 12-month development program. You will complete two six-month rotations, develop and evaluate quantitative solutions, work with large datasets, design and deploy models, and apply statistical techniques to support credit risk, financial crime, customer experience, and operations.
Requirements
- 2+ years of quantitative analytics experience or equivalent experience, training, military experience, or education
- Master's degree or higher in statistics, mathematics, physics, engineering, computer science, economics, or a quantitative discipline
- Currently pursuing a PhD with expected graduation between December 2026 and June 2027, or completing a postdoc after graduating from a PhD program after May 2024
- Programming skills with Python, R, SQL, Spark, and Java
- Quantitative and analytical skills
- Data analysis, modeling, visualization, statistics, research, and generative AI experience
- Data and software engineering skills
- Data management skills
- Communication skills
- Business acumen and understanding of capital markets
- Risk assessment and risk control experience
- Knowledge of machine learning and AI models, statistical modeling, data management, and computing
Responsibilities
- Forecast loss and revenue for credit card loan portfolios
- Develop credit scorecards for consumer decisioning strategies
- Build models to identify money laundering patterns across transaction databases
- Predict operational losses using statistical and machine learning modeling
- Validate model design, calibration, and implementation
- Design and deploy models for credit risk, financial crime, customer experience, and operations
- Apply quantitative techniques to analyze large datasets and generate insights
