Quantitative Analytics Consult 1

Wells Fargo Atlanta, GA

About the Job

Job Description

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 Modeling (CaPM) team is part of a Center of Excellence designed to improve model development.  This group is housed within the Chief Credit & Market Risk organization and is responsible for all credit risk and PPNR analytics for the firm.

CaPM is offering an exciting opportunity for a seasoned Quantitative Analytic with strong python programing skills. This position will utilize python based open source computing platform to implement statistical models built in SAS. This position requires critical debugging, testing and performance tuning abilities. This position will also work on state-of-the-art statistical methods to select features, build and optimize predictive models. The CaPM model development involves intensive team discussions, interactions with cross-functional teams, and dialogue with internal reviewers (Model Validation and Internal Audit). Strong communication skills will be essential to this role.

Key responsibilities include:

  • Utilize python based open source computing platform to implement or convert statistical models built in SAS.
  • Lead and participate in critical debugging, testing and performance tuning for statistical models written in python code
  • Responsible for state-of-the-art statistical methods to select features, build and optimize predictive models
  • Prepare ad-hoc analysis and reporting as requested
  • Responsible for documenting and presenting detailed model implementation processes and results, suitable for a variety of audiences
  • Lead and participate intensive team discussions, interactions with cross-functional teams, and dialogues with internal reviewers (Model Validation and Internal Audit)
  • Collaborate with key business models users to ensure models are business driven, properly implemented and run
  • Respond to ongoing analytical requests from auditors and regulatory reviewers


Required Qualifications

  • 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



Desired Qualifications

  • Excellent verbal, written, and interpersonal communication skills
  • Strong analytical skills with high attention to detail and accuracy
  • Ability to prioritize work, meet deadlines, achieve goals, and work under pressure in a dynamic and complex environment
  • Experience developing partnerships and collaborating with other business and functional areas



Other Desired Qualifications
  • A PhD in a quantitative discipline
  • 5+ years of credit risk modeling/analytical experience in banking or financial service industry
  • 3+ years of experience in SAS base, SAS macro, SAS SQL, and/or SAS stats, etc.
  • Proven ability to convert complex credit loss forecasting models from SAS to python, C++ or other object oriented programming languages
  • Knowledge of, and hands-on experience with, statistical model development/validation, utilizing best modeling practices and methodologies in the areas of data processing, sampling, model design/specification, model performance assessment, and evaluation testing Experience with data wrangling using pandas, pySpark, or numpy, etc.
     




Disclaimer


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.