The Investment & Corporate Bank (ICB) Risk Modelling team is responsible for developing statistical and machine learning models to reduce fraud and credit risk within ICB. The team also engages with external vendors and supports the onboarding of vendor models, including working through Model Governance to obtain appropriate approvals for different uses. The team executes and prepares model surveillance while providing insights for various regulatory requirements.

As an Applied AI Machine Learning Lead in the ICB Risk Modelling team, you will play a crucial role in analysing business problems and managing and developing machine learning models used to mitigate fraud risk within ICB. You will work closely with product owners, risk officers, data engineers, software engineers, and external vendors to ensure models perform effectively and meet the firm's standards. The primary focus will be on identity verification fraud, where you will lead efforts to adopt and implement advanced solutions, including models from leading vendors for detecting and preventing fraudulent activities. This work will be conducted in collaboration with counterparts in Chase US, ensuring consistency in approach and leveraging cross-regional expertise where applicable.

Job Responsibilities

• Assist product leadership in defining problem statements and execution roadmaps related to identity verification fraud models, ensuring alignment with business needs.
• Lead research and the evaluation of the state-of the-art models as well as adopt advanced solutions from leading vendors, to detect and prevent identity verification fraud and enhance automation and decision-making processes.
• Ensure robust model performance to meet the business expectations and the firm's governance standards. Perform root cause analysis for emerging trends in model performance, and communicate complex findings, insights, and recommendations to senior management and partners.
• Support the broader Risk Fraud Modelling initiative on synthetic data and synthetic IDs on behalf of the ICB business, contributing to the analysis, testing, and validation of synthetic data approaches.
• Work with multiple partner teams—including Strategy, Technology, Product Management, Legal, Compliance, Business Management, and Model Governance — to ensure the models meet the firm's high governance standards and regulatory requirements, and support audit and other business functions around model management.

Required Qualifications, Capabilities, and Skills

• Advanced degree (MSc or PhD) in a quantitative or technical discipline, or significant practical industry experience.
• Solid understanding of fraud modelling in financial organizations, including the unique challenges and regulatory considerations involved.
• Work experience in applied data science, machine learning techniques, with a strong understanding of both traditional statistical and machine learning models.
• Proficient in Python, with hands-on experience in data analysis and writing production-quality code.
• Extensive experience with machine learning and data analysis toolkits (e.g., NumPy, Scikit-Learn, Pandas).
• Ability to effectively leverage Generative AI tools to enhance productivity, analysis, and problem-solving in day-to-day work.
• Strong written and spoken communication skills to effectively convey technical concepts and results to both technical and business audiences. Team player.

Preferred Qualifications, Capabilities, and Skills

• Experience with ML model explainability and understanding model governance processes.
• Experience with model risk management frameworks.
• Experience with or strong interest in synthetic data generation, synthetic identity analysis, or related techniques.