Applied AI & ML Lead - Markets Operations

About the role

JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management. We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coa

Additional Information

Bring your expertise in applied artificial intelligence and machine learning to a team improving how Markets Operations runs at scale. You will partner across operations, product, engineering, and data to deliver production-grade solutions with measurable impact. In a collaborative environment, you will shape strategy, mentor talent, and build durable capabilities designed for reliability, control, and real-world adoption. As an Applied Artificial Intelligence and Machine Learning Lead at JPMorganChase within Markets Operations in the Commercial & Investment Bank , you will drive the strategy, design, and delivery of solutions that improve operational efficiency, resilience, and control outcomes. You will lead a team of scientists and engineers and collaborate with senior stakeholders to prioritize high-impact opportunities and deliver solutions that scale. You will set technical direction, strengthen engineering and governance practices, and translate complex concepts into clear, outcome-focused results. Job Responsibilities: Lead the end-to-end delivery of machine learning and generative AI solutions that measurably improve Markets Operations outcomes Set technical direction and execution strategy across model development, deployment, and adoption aligned to business priorities Oversee the architecture and production deployment of AI applications, including agent-based and workflow-automation solutions Manage, coach, and develop a team of scientists and engineers, fostering a collaborative and inclusive culture of continuous learning Establish and enforce best practices for model monitoring, evaluation, and performance optimization in production environments Partner with business, operations, and technology leaders to shape problem statements, success metrics, and delivery roadmaps Guide analysis of large, complex datasets to identify drivers, risks, and automation opportunities Ensure solutions are engineered for reliability, scalability, and maintainability, with strong operational readiness Communicate technical approaches, decisions, and results through clear documentation and cross-functional forums Required Qualifications, Capabilities, and Skills: Formal training or certification on applied artificial intelligence and machine learning concepts and 5+ years applied experience Bachelor's or Master's degree in computer science, data science, artificial intelligence, or a related field (or equivalent experience) Demonstrated leadership delivering AI/ML initiatives from concept through production, including team and stakeholder management Strong applied experience in machine learning, including feature engineering, model development, and statistical analysis on large datasets Hands-on proficiency in Python and common machine learning libraries (for example, scikit-learn, TensorFlow, or PyTorch) Proven experience deploying, operating, and maintaining production machine learning systems, including incident and performance ownership Working knowledge of machine learning operations practices (machine learning lifecycle management, automation, and reproducibility) Experience building and deploying generative AI applications and evaluating large language model outputs for quality and risk Strong communication skills, including the ability to translate technical concepts for senior stakeholders and non-technical partners Preferred Qualifications, Capabilities, and Skills: Doctorate in a quantitative field or equivalent advanced applied research experience Experience delivering AI/ML solutions in financial services, capital markets, or operations-focused environments Experience working in highly regulated environments with strong model risk, governance, or control expectations Experience designing scalable system architectures for AI products and platforms across multiple stakeholder groups #CIBAppliedAI

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