Join us at the forefront of payments innovation, where your expertise in machine learning will shape the future of global finance. You will have the opportunity to deliver meaningful impact, collaborate with talented teams, and grow your career in a dynamic environment. We value your unique perspective and commitment to excellence. At JPMorganChase, you can push the boundaries of what’s possible and help connect businesses and consumers worldwide.
As a Senior Machine Learning Data Scientist – Payments (VP) on the Payments Machine Learning team, you will lead the end-to-end delivery of advanced machine learning applications. You will work closely with cross-functional partners to drive measurable business outcomes and mentor others in best practices. You will help shape the team’s culture of innovation, collaboration, and continuous learning. Your work will directly influence the evolution of payments technology and its impact on the global economy.
Job Responsibilities:
- Lead end-to-end delivery of machine learning and AI solutions for complex Payments and Banking Operations challenges, from discovery to production rollout and lifecycle management.
- Develop innovative ML-based solutions, including GenAI and agentic approaches, and define evaluation, safety, and monitoring strategies for production use.
- Own production deployment patterns, including containerization, CI/CD, automated testing, model registries, governance, monitoring, alerting, and rollback strategies.
- Architect and deploy scalable, reliable, and secure ML services integrated with strategic platforms and downstream consumers (APIs, batch, streaming), meeting SLAs and SLOs.
- Partner with product, operations, risk/control, and technology teams to influence roadmaps, align on requirements, and deliver data-driven transformations.
- Establish reusable, modular data science and machine learning capabilities and patterns scalable across multiple use cases.
- Provide technical leadership and mentorship through code reviews, design reviews, best practices, and upskilling across data science and engineering partners.
- Communicate clearly with technical and non-technical stakeholders, translating model outputs into actionable decisions 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 (e.g., Data Science, Computer Science, Applied Mathematics, Statistics, Econometrics) or Bachelor’s degree with equivalent relevant experience.
- Deep understanding of machine learning and AI fundamentals, with strong applied data analysis skills and experience with rigorous evaluation and measurement in real-world settings.
- Proven experience deploying and operating machine learning models in production at scale, including observability, reliability, incident management, and continuous improvement.
- Proficiency in Python software engineering, including production-grade, modular OOP design, testing, performance tuning, and debugging.
- Familiarity with MLOps and distributed systems, including training and serving patterns, batch and real-time architectures, feature stores, orchestration, and scalable data processing.
- Ability to design 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, security, and audit-ready documentation.
- Strong problem-solving, communication, stakeholder management, and teamwork skills, with a results-driven mindset and client focus.
Preferred Qualifications, Capabilities, and Skills:
- Experience with NLP and/or GenAI (LLMs, retrieval-augmented generation, tool/function calling, agentic workflows), including evaluation and safety patterns.
- Expertise with machine learning frameworks and data science packages (e.g., PyTorch, TensorFlow, Scikit-Learn, NumPy, Pandas, SciPy, statsmodels).
- Experience deploying to AWS (e.g., SageMaker, Bedrock) and operating production workloads with attention to cost, performance, security, and scaling.
- Experience integrating human-in-the-loop or user feedback signals into iterative improvement processes.
If you’re ready to make a lasting impact in a fast-evolving industry and grow your career with a diverse, collaborative team, we invite you to apply and join us on this exciting journey.