Credit Risk Modelling & Innovation VP - Corporate & TCIO

Role Overview: As a Vice President in the Credit Risk Innovation team at our company, you will play a crucial role in developing next-generation solutions for credit risk analytics and portfolio management. You will be responsible for leading the development and implementation of advanced risk models, modernizing the operating model of the team, and optimizing workflows to embed modeling into business decisioning. Our culture encourages thinking outside the box, challenging the status quo, and striving to be best-in-class. Key Responsibilities: - Build, enhance, and implement credit risk models for stress testing, risk appetite, IFRS9, and CECL, ensuring compliance with regulatory standards and alignment with business objectives. - Architect and develop solutions using Python/PySpark, cloud technologies (AWS, Azure), and modern front-end frameworks (React, Angular) to deliver robust credit risk tools. - Integrate advanced risk models and analytics into product and portfolio management, ensuring data-driven, scalable, and regulatory-compliant solutions. - Partner with cross-functional teams to deliver impactful solutions, drive adoption, and automate risk analytics processes using Generative AI/LLMs, Machine Learning, and modern technology stacks. - Develop product proposals, manage backlogs, prioritize features, and communicate progress to stakeholders. - Work in cross-functional squads, apply agile methodologies (Scrum, Kanban), and iterate quickly based on stakeholder feedback. - Prepare model documentation, support model governance, and maintain strong control standards throughout the development lifecycle. Qualifications Required: - 7+ years of experience in leading model development, risk analytics, data science, and product development. - Proven experience in developing and executing credit risk processes for stress testing, risk appetite, and IFRS9/CECL. - Strong statistical modeling skills, including regression analysis, time series modeling, and probability theory applied to risk analytics. - Advanced Python/PySpark programming skills with hands-on experience in model implementation and solution development. - Solid understanding of regulatory requirements and credit risk analytics in banking. - Experience designing and delivering analytics products, data pipelines, and dashboarding. - Ability to innovate, automate, and optimize risk processes using technology. - Excellent problem-solving, communication, and stakeholder management skills. - Hands-on experience with agile frameworks, sprint planning, and backlog management. - Master's or advanced degree in a quantitative field (e.g., mathematics, statistics, engineering, computer science, finance). (Note: Preferred qualifications, capabilities, and skills were not included in the provided Job Description.),