Lead Quantitative Model Solutions Specialist

Job description

**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.