Quantitative Analytics Specialist 2Job ID 5549870
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At Wells Fargo, we want to satisfy our customers’ financial needs and help them succeed financially. 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. The team focuses on several key risk types, including conduct, credit, financial crimes, information security, interest rate, liquidity, market, model, operational, regulatory compliance, reputation, strategic, and technology risk.
The group provides leadership, enhances communications, assists with problem identification and solutions, and shares best practices. In addition, the group provides an enterprise-wide view of risk, assists management and our Board of Directors in identifying and monitoring risks that may affect multiple lines of business, and takes 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 stress-testing under the Comprehensive Capital Analysis and Reporting exercises (CCAR) and nine-quarter business forecasting.
- Credit loss estimation models for the entire loan portfolio to support allowance for credit loss, stress testing, and Basel.
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 development for non-interest income and expense forecasting. The team is responsible for developing, documenting and supporting models and results. The selected candidate 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.
Our ideal candidate will have a sound background and understanding of PPNR modeling including a strong understanding of modeling techniques like OLS and generalized linear models including logistic regression, hazard models, time-series, and panel regression. The same candidate would be able to bridge between parametric approaches to analysis and machine learning techniques. She/he will appreciate the use of modeling approaches such as penalized regression, spline-fitting, or spectral analysis and how they relate to models built using piecewise regression or some other extension of the linear model tradition.
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 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 address 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
Other Desired Qualifications
- Excellent computer programing skills and use of statistical software packages such as Python, R, SAS and SQL
- Knowledge of time-series regression and forecasting models
- Experience implementing and coding large and complex models
- Proven written and oral communication skills as well as interpersonal skills
- Experience and ability to demonstrate first-hand knowledge in the areas of data analytics, modeling, statistical inference, computing, big data and machine learning
- 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 highly demanding environment while balancing multiple priorities
- Understanding of bank regulatory data sets and other industry data sources
- A willingness to take a lead role on projects, while functioning effectively as an individual contributor.
- An understanding of the importance of, and the ability to manage to, deadlines in an environment where there are multiple dependencies on the outcome of one’s work is essential.
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.