Lead Quantitative Model Solutions Specialist

About this role:

Wells Fargo is seeking a Lead Quantitative Model Solutions Specialist.

In this role, you will:

• Lead complex, large-scale model maintenance, optimization, and planning initiatives related to operational processes, controls, reporting, testing, implementation, and documentation
• Review and analyze complex multi-faceted model operations and optimization challenges that require in-depth evaluation of multiple factors including intangibles or unprecedented factors
• Develop model processes and optimization strategies for short- and long-term objectives; support and provide insights regarding a wide array of business initiatives
• Make decisions in complex and multi-faceted situations requiring solid understanding of agile development
• Influence global assessment of model maintenance schedules inclusive of engineering, structure, and scope of review following the System Development Life Cycle process, quality, security, and compliance requirements
• Strategically collaborate and consult with peers, colleagues, and managers to resolve issues and achieve goals

Required Qualifications:

• 5+ years of quantitative model solutions or quantitative model operations experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education

Desired Qualifications:

• Bachelors/masters degree in quantitative finance, Computer Science, Engineering, or related field.
• 5+ years of experience in model implementation or quantitative analytics within banking or financial services.
• Strong programming skills in Python and PySpark
• Proven experience in Implementation/Development of regulatory Credit risk (including CCAR, CECL and IFRS), RRP Valuation, and PPNR models.
• Familiarity with version control (Git), CI/CD pipelines, and cloud platforms.

Job Expectations:

• Lead end-to-end implementation of regulatory credit risk models (PD, LGD, EAD) into production systems.
• Collaborate with model development, validation, and business teams to ensure accurate and efficient model deployment.
• Design and optimize scalable data pipelines using PySpark and distributed computing frameworks.
• Develop robust, well-documented code in Python for model execution and integration.
• Ensure compliance with regulatory standards (Basel, IFRS9, CCAR) during implementation.
• Perform rigorous testing, back-testing, and benchmarking of implemented models.
• Provide technical leadership and mentorship to junior team members.