Applied AI and Machine Learning Associate

Role Overview: As an AI/ML Associate in the ICB Risk Modelling team at JPMorgan Chase, you will be an integral part of the team responsible for developing statistical and machine learning models to reduce fraud and credit risk within the International Consumer Banking division. Your role will involve working closely with various stakeholders to onboard, develop, and manage machine learning models used to mitigate fraud and credit risk. You will play a crucial role in analyzing business problems and ensuring that the models meet the firm's high standards. Key Responsibilities: - Own end-to-end onboarding and lifecycle management of vendor models used for fraud and credit risk decisioning. - Evaluate and adopt state-of-the-art models and vendor capabilities for fraud and credit risk, providing effective challenge through independent assessment. - Develop in-house fraud and credit risk models end-to-end, applying statistical and machine learning techniques across feature engineering, model training, testing, and deployment readiness. - Design and execute ongoing performance monitoring for vendor and in-house models, tracking stability, drift, and outcomes. - Conduct root-cause analysis for emerging trends and performance shifts, quantify business impact, and communicate findings and recommendations to senior management. - Prepare and maintain governance, audit, and regulatory materials supporting vendor/in-house model oversight. - Collaborate with multiple partner teams to ensure models meet high governance standards and regulatory requirements. - Work on multiple projects with modeling teams in other locations to ensure high-quality model development standards and compliance with firm's Estimation policies. Qualifications Required: - 4+ years statistical machine learning model development/validation experience in the financial services industry or Fintech. - Masters or Ph.D. degree in a technical or quantitative field. - Solid understanding of fraud and/or credit risk modeling in financial organizations. - Proficient in Python, with hands-on experience in data analysis and writing production-quality code. - Extensive experience with machine learning and data analysis toolkits. - Ability to effectively leverage Generative AI tools. - Strong written and spoken communication skills. - Team player. Additional Details: JPMorgan Chase is a leading financial institution offering innovative financial solutions to consumers, businesses, and institutional clients globally. With a history spanning over 200 years, the company is a leader in various financial services. JPMorgan Chase values diversity and inclusion, being an equal opportunity employer that does not discriminate based on any protected attribute. The company also provides reasonable accommodations for applicants' and employees' needs related to religious practices, beliefs, mental health, or physical disabilities. Role Overview: As an AI/ML Associate in the ICB Risk Modelling team at JPMorgan Chase, you will be an integral part of the team responsible for developing statistical and machine learning models to reduce fraud and credit risk within the International Consumer Banking division. Your role will involve working closely with various stakeholders to onboard, develop, and manage machine learning models used to mitigate fraud and credit risk. You will play a crucial role in analyzing business problems and ensuring that the models meet the firm's high standards. Key Responsibilities: - Own end-to-end onboarding and lifecycle management of vendor models used for fraud and credit risk decisioning. - Evaluate and adopt state-of-the-art models and vendor capabilities for fraud and credit risk, providing effective challenge through independent assessment. - Develop in-house fraud and credit risk models end-to-end, applying statistical and machine learning techniques across feature engineering, model training, testing, and deployment readiness. - Design and execute ongoing performance monitoring for vendor and in-house models, tracking stability, drift, and outcomes. - Conduct root-cause analysis for emerging trends and performance shifts, quantify business impact, and communicate findings and recommendations to senior management. - Prepare and maintain governance, audit, and regulatory materials supporting vendor/in-house model oversight. - Collaborate with multiple partner teams to ensure models meet high governance standards and regulatory requirements. - Work on multiple projects with modeling teams in other locations to ensure high-quality model development standards and compliance with firm's Estimation policies. Qualifications Required: - 4+ years statistical machine learning model development/validation experience in the financial services industry or Fintech. - Masters or Ph.D. degree in a technical or quantitative field. - Solid understanding of fraud and/or credit risk modeling in financial organizations. - Proficient in Python, with hands-on