The ICB (International Consumer Banking) business within JPMorgan Chase has grown significantly since its launch in 2021, and we expect the business to expand further over the next few years. Join the expansion of the Chase digital bank across the UK and Europe, and help us continue to build our award-winning bank.

The 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 AI/ML Associate in the ICB Risk Modelling team, you will play a crucial role in analysing business problems and onboarding/developing and managing machine learning vendor/inhouse models used to mitigate fraud & credit 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.

We are looking for someone with a strong foundation in statistics, machine learning, and data analysis, who can understand and support the strategic objective and data landscape of large, complex organisations.

Job 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 of model methodology, assumptions, inputs, limitations, and performance etc.
Develop in-house fraud and credit risk models end-to-end, applying statistical and machine learning techniques (e.g., regression, XGBoost/LightGBM, neural networks etc.) across feature engineering, model training, testing/benchmarking, and deployment readiness.
Design and execute ongoing performance monitoring for vendor and in-house models, leveraging multi-dimensional aggregation (e.g., time, geography, portfolio/segment, channel, customer and transaction/loan attributes) to track stability, drift, and outcomes. Conduct root-cause analysis for emerging trends and performance shifts, quantify business impact, and communicate clear findings and recommendations to senior management and partners.
Prepare and maintain governance, audit, and regulatory materials supporting vendor/inhouse model oversight, model surveillance, and required documentation standards.
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.
Expected to work on multiple projects with modeling teams in other locations, to ensure high quality model development standards, reviews and re-reviews, model monitoring, enhanced model usage support etc. in compliance with firm’s Estimation policies and procedures. Ensure right development and usage of models owned.

Required Qualifications, Capabilities, and Skills

4+ years’ statistical Machine learning model development/validation experience in a deeply quantitative role in the financial services industry or Fintech’s dealing with advanced analytical or machine learning methods.
A Master’s or Ph.D. Degree in a technical or quantitative field such as Statistics, Economics, Finance, Mathematics, Computer Science, Engineering from Top-Tier university
Solid understanding of fraud and/or credit risk modelling in financial organizations, including the unique challenges and regulatory considerations involved.
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 development and understanding model governance processes.
Experience with model risk management frameworks.

Job 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 of model methodology, assumptions, inputs, limitations, and performance etc.
Develop in-house fraud and credit risk models end-to-end, applying statistical and machine learning techniques (e.g., regression, XGBoost/LightGBM, neural networks etc.) across feature engineering, model training, testing/benchmarking, and deployment readiness.
Design and execute ongoing performance monitoring for vendor and in-house models, leveraging multi-dimensional aggregation (e.g., time, geography, portfolio/segment, channel, customer and transaction/loan attributes) to track stability, drift, and outcomes. Conduct root-cause analysis for emerging trends and performance shifts, quantify business impact, and communicate clear findings and recommendations to senior management and partners.
Prepare and maintain governance, audit, and regulatory materials supporting vendor/inhouse model oversight, model surveillance, and required documentation standards.
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.
Expected to work on multiple projects with modeling teams in other locations, to ensure high quality model development standards, reviews and re-reviews, model monitoring, enhanced model usage support etc. in compliance with firm’s Estimation policies and procedures. Ensure right development and usage of models owned.

Required Qualifications, Capabilities, and Skills

4+ years’ statistical Machine learning model development/validation experience in a deeply quantitative role in the financial services industry or Fintech’s dealing with advanced analytical or machine learning methods.
A Master’s or Ph.D. Degree in a technical or quantitative field such as Statistics, Economics, Finance, Mathematics, Computer Science, Engineering from Top-Tier university
Solid understanding of fraud and/or credit risk modelling in financial organizations, including the unique challenges and regulatory considerations involved.
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 development and understanding model governance processes.
Experience with model risk management frameworks.