Senior Quantitative Analytics Specialist, Credit Risk Modeling & Data Analytics

Wells Fargo Bank•Charlotte, NC

4d•Hybrid

About The Position

Wells Fargo is seeking a Senior Quantitative Analytics Specialist (Senior Assistant Vice President) to support the development, implementation, and enhancement of credit risk models used for risk measurement, portfolio management, forecasting, and strategic decision-making. This role requires strong quantitative expertise, advanced programming skills, and the ability to communicate complex modeling concepts to technical and non-technical stakeholders. The successful candidate will work with large and complex datasets, apply statistical and machine learning techniques, and contribute to a strong model governance framework.

Requirements

4+ years of Quantitative Analytics experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
Bachelor's degree or higher in a quantitative discipline such as mathematics, statistics, engineering, physics, economics, or computer science

Nice To Haves

Demonstrated expertise in developing, implementing, and monitoring credit risk models using statistical, econometric, and machine learning techniques to support risk measurement, forecasting, portfolio management, and business decision-making.
Experience developing or supporting Probability of Default (PD), Loss Given Default (LGD), Exposure at Default (EAD), CECL, stress testing, portfolio risk, or other credit risk models.
Experience applying advanced statistical and machine learning methodologies to solve complex risk management and predictive analytics challenges.
Strong programming and analytical skills with hands-on experience using Python, SAS, R, SQL, Spark, or similar big data technologies.
Experience analyzing, transforming, and managing large, complex datasets to support model development, implementation, monitoring, and reporting.
Ability to leverage data analytics and scalable computing frameworks to develop efficient analytical solutions and improve model performance.
Strong understanding of model governance practices, including model documentation, validation support, audit responses, and regulatory interactions.
Proven ability to document, defend, and communicate model methodologies, assumptions, limitations, performance results, and recommendations to validators, auditors, regulators, senior leadership, and other technical stakeholders.
Excellent verbal and written communication skills, with the ability to translate complex quantitative concepts into clear business insights.

Responsibilities

Develop, enhance and monitor credit risk models using advanced statistical, econometric, and machine learning methodologies to support business and risk management objectives.
Perform advanced coding, data analysis, and statistical modeling using Python, SQL, SAS, R, Spark, and other analytical tools to evaluate large complex datasets to identify risk trends, portfolio behavior, and emerging risks.
Design and execute model development strategies, including data preparation, feature engineering, model estimation, performance testing, and ongoing monitoring.
Perform quantitative analyses to support credit risk measurement, loss forecasting, stress testing, and portfolio management initiatives.
Partner with risk managers, business stakeholders, and technology teams to deliver scalable and impactful analytical solutions.
Document model development processes, assumptions, methodologies, testing results, and performance metrics in accordance with model governance standards.
Prepare and present model results, technical findings, and recommendations to model validators, auditors, regulators, and senior management.
Respond to model review findings and provide support during model validation, audit, and regulatory examinations.
Evaluate emerging statistical and machine learning techniques and recommend innovative approaches to enhance model performance and risk measurement capabilities.
Ensure compliance with Wells Fargo's model risk management framework, governance policies, and regulatory expectations.
Collaborate with cross-functional teams to improve analytical processes, enhance automation, and drive efficiencies in model development and reporting.