Senior Quantitative Analytics Specialist, Credit Risk Modeling & Data Analytics

We need to output HTML fragment with sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections where there is real specific content. Must not repeat job title. Must not include Job ID etc. Must remove boilerplate. Must preserve all substantive info. We need to parse the posting. Content: - About this role: description: "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." We can put that under

About the role

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. - Responsibilities: bullet list of duties: "In this role, you will:" then list items. List items: - 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. We'll put under

Responsibilities

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  . The instruction: Use headings for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. So we can separate Required Qualifications under Requirements, Desired Qualifications under Qualifications (or Skills). We need to decide. Desired Qualifications bullet list: - 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. We can put under

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  Pay

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  stating the base pay range and note about total rewards. - Benefits: list of benefits: Health benefits, 401(k) Plan, Paid time off, Disability benefits, Life insurance, critical illness insurance, and accident insurance, Parental leave, Critical caregiving leave, Discounts and savings, Commuter benefits, Tuition reimbursement, Scholarships for dependent children, Adoption reimbursement. We'll put under

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  with the three locations. We also need to include "Job Expectations: This position offers a hybrid work schedule. Willingness to work onsite at stated location on the job posting. This position is eligible for VISA sponsorship." That's schedule and visa. We'll put under Schedule. We must not include "Reflected is the base pay range offered for this position. Pay may vary depending on factors..." etc. That's part of Pay. We must not include "Information will be shared about expectations during the recruitment process". That's boilerplate. We must not include "Job Posting Locations:" line maybe we can incorporate. We must not include "Posting End Date: 30 Sep 2026 * Job posting may come down early due to volume of applicants." That's boilerplate? It's date; maybe minor metadata. Could be dropped. But preserving all substantive info? The date is not essential for role; but it's info. However they said we can drop minor metadata. I'd drop it. We must not include EEO etc. Now produce HTML fragment. Structure:

  Locations: Three Wells Fargo Center, 401 S Tryon St, CHARLOTTE, NC; Las Colinas Bldg B, Irving Campus, IRVING, TX; West Des Moines Campus, WEST DES MOINES, IA; 600 Wells Fargo, Minneapolis, MN.

  Then sections. Let's craft.

  About the role

  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.

  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.

  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.

  Qualifications

  • 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.

  Schedule