At Wells Fargo, we have one goal: to satisfy our customers’ financial needs and help them achieve their dreams. We’re looking for talented people who will put our customers at the center of everything we do. Join our diverse and inclusive team where you’ll feel valued and inspired to contribute your unique skills and experience.
Help us build a better Wells Fargo. It all begins with outstanding talent. It all begins with you.
Corporate Risk helps all Wells Fargo businesses identify and manage risk. We focus on three key risk areas: credit risk, operational risk and market risk. We help our management and Board of Directors identify and monitor risks that may affect multiple lines of business, and take appropriate action when business activities exceed the risk tolerance of the company.
The Credit and PPNR (CaPM) Model Development Team (the "team") is a unit within Corporate Credit and is responsible for model development and implementation of the following model types:
- Pre-Provision Net Revenue (PPNR) estimates, including forecasting models, to support Dodd Frank and the Comprehensive Capital Analysis and Reporting exercises (CCAR).
- Credit loss estimation models for the entire loan portfolio to support allowance for credit loss (including current expected credit loss preparation); estimation of risk weighted assets (RWA) in compliance with BASEL regulations; and, economically sensitive credit loss estimation in compliance with Dodd Frank and the Comprehensive Capital Analysis and Reporting exercises (CCAR).
The team is seeking a dynamic individual with experience in predictive modeling and data analysis to join the model development team focusing on PPNR model requirements for non-interest income and expense forecasting. The team is responsible for developing , documenting and supporting models and results. This position requires application of analytical, statistical modeling, and forecasting methods and informing the analysis with the theory and mathematics behind the methods.
Our ideal candidate will have a sound background and understanding of PPNR modeling including a strong understanding of modeling techniques like generalized linear models, logistic regression, hazard models, time series, pooled cross-section and time-series models, and Monte Carlo simulation.
She/he will be able to articulate the strengths and weaknesses of various predictive modeling techniques and have a strong understanding of statistical testing necessary to assess model performance. The candidate must be able to bridge the gap between theory and practice to deliver projects suitable for the intended business purpose – stress testing and business forecasts of fee revenue and expense.
Ideally, this individual will also have experience and knowledge with the components of bank income statements including trading gains and losses.
The duties of this position will include, but not be limited to the following:
- Developing non-interest income and expense forecasting models
- Integration of forecasts with existing balance forecasting models and stress testing processes
- Create long-form and presentation documents to explain the model results to both technical and non-technical audiences
- Develop and document models to forecast conditional results indicative of both Wells Fargo and industry level performance
- Work closely with line of business partners to develop and enhance the theory and business logic behind the models and forecasts; address data and model questions from our partners, model validation, and regulators.
- Data research to facilitate modeling and analysis
- Adhere to model validation governance to ensure models are in compliance with policy and are working as intended, address model validation and regulatory feedback issues
- Coherently articulate analysis results to business partners, model validation, audit and regulators
- Support ad hoc analytic projects
- 2+ years of experience in an advanced scientific or mathematical field
- A master's degree or higher in a quantitative field such as mathematics, statistics, engineering, physics, economics, or computer science
- A PhD in a quantitative discipline
- Excellent verbal, written, and interpersonal communication skills
- An active Chartered Financial Analyst (CFA) designation
- Strong SAS programming and SQL experience
- Programming skills in Python and R
- Extensive knowledge of time-series regression and ARIMA models
- Experience using SAS ETS
- Experience driving balance sheet, fee income, and/or expense modeling and strategy
- Experience implementing and coding large and complex models
- A demonstrated track-record of delivering quantitative projects on-time
- Knowledge of bank products across consumer, wholesale, and trust and investment
- Detail oriented, results driven, and has the ability to navigate in a quickly changing and high demand environment while balancing multiple priorities
- Understanding of bank regulatory data sets and other industry data sources
- All offers for employment with Wells Fargo are contingent upon the candidate having successfully completed a criminal background check. Wells Fargo will consider qualified candidates with criminal histories in a manner consistent with the requirements of applicable local, state and Federal law, including Section 19 of the Federal Deposit Insurance Act.
Relevant military experience is considered for veterans and transitioning service men and women.
Wells Fargo is an Affirmative Action and Equal Opportunity Employer, Minority/Female/Disabled/Veteran/Gender Identity/Sexual Orientation.