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:

• Bachelor’s/master’s 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.