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Applied AI ML Data Scientist, Vice President - Payments

JPMorgan Chase
London, GBR
Full-time
Senior
Onsite
Discovered 2 weeks ago
PythonMLOpsLLMOpsMCPPyTorchTensorFlow
Free

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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

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