Credit Risk Modelling

JOB DESCRIPTION

Are you passionate about building innovative products that transform risk management in banking? Join our Credit Risk Innovation team where you'll combine quantitative modelling, product development, agile delivery, and techno-functional expertise to create next-generation solutions for credit risk analytics and portfolio management. 

As an Vice President in the Credit Risk, you are at the centre of keeping JPMorgan Chase strong and resilient. You will help the firm grow its business in a responsible way by anticipating new and emerging risks. In this role you will lead the development and implementation of advanced risk models for stress testing, risk appetite, IFRS9, and CECL. Using your python programming, AI/ML and analytics solution development skills you will modernise the operating model of the team to optimize our workflows and embed modelling into business decisioning. Our culture is all about thinking outside the box, challenging the status quo and striving to be best-in-class.

Job Responsibilities

• Build, enhance, and implement credit risk (PD, LGD, EAD etc.) 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, awareness of 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 solutions are data-driven, scalable, and regulatory-compliant.
•  Partner with risk managers, product, and tech teams across geographies to deliver impactful solutions and drive adoption.
•  Identify opportunities to automate and streamline risk analytics processes using Generative AI/LLMs, Machine Learning, data engineering, 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.

Required qualifications, capabilities, and skills

• 7+ years' experience in leading model development, implementation, risk analytics, data science and product development.
• Proven experience in developing and execution of credit risk processes for stress testing, risk appetite and IFRS9/CECL, ideally covering both corporate and securitized products.
• Ability to break down complex business challenges and deliver practical, scalable solutions.
• Strong statistical modelling skills, including expertise in techniques such as regression analysis, time series modelling, and probability theory as 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 or platforms, including 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.
• Skilled at translating technical concepts for business stakeholders and driving cross-team collaboration.
• Master's or advanced degree in a quantitative field (e.g., mathematics, statistics, engineering, computer science, finance).

Preferred qualifications, capabilities, and skills

• Experience in large banking, consulting and financial services is a plus.
• Experience implementing, fine-tuning, and integrating AI/ML models with cloud platforms and cloud-native services.
• Practical experience in leveraging Generative AI, large language models (LLMs), and driving automation in financial services.
• Understanding of MLOps practices for deploying, monitoring, and maintaining AI solutions in banking.
• Working knowledge of agile tools (JIRA, Confluence) and project management methodologies.