Join J.P. Morgan Corporate Investment Bank's industry-leading AI team, where you'll combine cutting-edge machine learning techniques with unique data assets to optimize business decisions. 

 

As an Applied AI & Machine Learning Associate within the Markets Operations team, you will be responsible for advancing financial applications from business intelligence to predictive models and automated decision-making. Your role will involve producing and maintaining technical documentation for governance purposes, serving as a technical liaison, and monitoring performance metrics. You will also collaborate with the team to provide operational solutions for the Corporate & Investment Bank's Markets business across all financial asset classes.

 

Job Responsibilities:

Produce and maintain comprehensive technical documentation for governance purposes, detailing the internal workings, end-to-end deployment, and usage of production machine learning models.Serve as the primary technical liaison between the AI team and governance-related functions, such as model risk, controls, data use, legal, and audit.Monitor ongoing performance metrics and identify instances of data drift.Manage and preserve artifacts, such as datasets, model files, configurations, and evaluation experiments, to ensure the reproducibility of production models and facilitate audit processes.Coordinate the approval process for accessing services essential for AI model development and deployment.Participate in audits related to production AI models.Assist with hands-on development and deployment of AI models.Stay informed about the latest trends and advancements in AI technologies.

 

Required Qualifications, Capabilities, and Skills:

Master’s degree or equivalent experience in a quantitative discipline such as Computer Science, Artificial Intelligence, Machine Learning, Data Science, Statistics, Mathematics, or Physics.Extensive experience in documenting and effectively communicating technical work.Experience within the Financial Services industry.Strong understanding of machine learning model architectures and the associated risks.Proficiency in metrics, benchmarking, and evaluation methodologies for AI-driven, user-facing products, including ongoing performance monitoring.Hands-on experience with building AI models.Familiarity with software development concepts and technologies, with a focus on AI applications.Knowledge of AI governance, encompassing model risk, controls, data use, and legal considerations.Exceptional communication skills, with the ability to convey complex technical details to non-technical audiences.

 

Preferred Qualifications, Capabilities, and Skills:

Experience with governance specific to Generative AI.