Applied AI ML Data Scientist, Vice President - Payments

J.P. Morgan’s Commercial & Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world. 

Payments sit at the center of the global economy, connecting businesses and consumers around the world. You will join a team driving innovation in this fast evolving space as new technologies reshape how money moves. In this role, you will work at the forefront of modern machine learning and generative AI to deliver meaningful, lasting impact on global finance. You will partner closely with product, operations, risk, and technology teams to turn ideas into measurable outcomes. You will also have opportunities to mentor others and help shape how we build and operate production AI.

As a Vice President Machine Learning Data Scientist in Payments Machine Learning, you will lead the design, development, and production deployment of machine learning applications, including generative AI and agentic systems, on cloud infrastructure. You will own end-to-end delivery from problem framing and experimental design through scalable MLOps, evaluation, governance, and integration with strategic systems. You will help set technical direction, establish reusable patterns, and ensure solutions are reliable, secure, and observable in production. You will mentor engineers and data scientists and translate complex technical work into clear decisions and outcomes for stakeholders.

Job Responsibilities

• Lead end-to-end delivery of machine learning and AI solutions for complex payments and banking operations problems, from discovery and framing to production rollout and lifecycle management
• Develop innovative machine learning solutions, including generative AI and multi-agent approaches, and define evaluation, safety, and monitoring strategies for production use
• Own production deployment patterns including containerization, continuous integration and delivery, automated testing, model and prompt registries, model and version governance, monitoring and alerting, and rollback strategies
• Architect and deploy scalable, reliable, and secure machine learning and large language model services integrated with strategic platforms and downstream consumers across APIs, batch, streaming, and event-driven patterns, meeting service level objectives
• Partner with product, operations, risk and control, and technology teams to influence roadmaps, align on requirements, and deliver data-led transformations
• Establish reusable, modular data science and machine learning capabilities that scale across use cases, including feature engineering, evaluation harnesses, prompt tooling patterns, agent frameworks, orchestration, and context and memory management
• Provide technical leadership and mentorship through code reviews, design reviews, best practices, and upskilling across data science and engineering partners
• Communicate with technical and non-technical stakeholders, translating model outputs into decisions, tradeoffs, and operational plans
• Maintain strong documentation for approaches, model cards, runbooks, and operational procedures

Required Qualifications, Capabilities, and Skills

• Master’s degree in a quantitative field, or equivalent practical experience
• Deep understanding of machine learning fundamentals with strong applied data analysis skills
• Demonstrated experience designing rigorous evaluation and measurement in real-world settings
• Demonstrated experience deploying and operating machine learning models in production at scale, including monitoring, drift and performance management, reliability, incident management, and continuous improvement
• Strong Python software engineering skills, including modular object-oriented design, testing, performance tuning, and debugging
• Working knowledge of MLOps and LLMOps and distributed systems, including training and serving patterns, batch versus real-time architectures, feature stores, orchestration, and scalable data processing
• Ability to design intrinsic and extrinsic evaluations aligned with business goals, including offline and online alignment and guardrails for unintended outcomes
• Experience working in regulated environments with awareness of model risk, controls, privacy and security, and audit-ready documentation
• Strong stakeholder management and teamwork skills, with the ability to drive outcomes in partnership with cross-functional teams

Preferred Qualifications, Capabilities, and Skills

• Experience with NLP and generative AI, including large language models, retrieval-augmented generation, tool and function calling, agentic workflows, multi-agent orchestration, and related evaluation and safety patterns
• Familiarity with agentic building blocks and standards, including orchestration frameworks, context and memory management, and interoperability protocols such as MCP
• Experience with machine learning frameworks and data science packages such as PyTorch, TensorFlow, scikit-learn, NumPy, pandas, SciPy, and statsmodels
• Experience deploying to AWS, including services such as SageMaker and Bedrock, and operating production large language model and machine learning workloads with attention to cost, latency, performance, security, and scaling
• Experience integrating human-in-the-loop and user feedback signals into iterative improvement, including active learning, preference signals, and labeling strategies