Applied AI ML Data Scientist, Vice President - Payments
Salary not listed • LONDON, LONDON, United Kingdom • Full-time
Posted 4d ago
Job Location
LONDON, LONDON, United Kingdom
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
AI Engineer
About the job
Join a dynamic team at the forefront of financial innovation, leveraging modern machine learning and generative AI to reshape global finance. This role involves partnering closely with product, operations, risk, and technology teams to transform ideas into measurable outcomes. You will have the opportunity to mentor others and influence how production AI is built and operated within the payments sector. As a Vice President Machine Learning Data Scientist, 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 the entire delivery lifecycle, from problem framing and experimental design to scalable MLOps, evaluation, governance, and integration with strategic systems, while also setting technical direction and ensuring reliable, secure, and observable solutions.
Responsibilities
- Lead end-to-end delivery of ML and AI solutions for payments and banking operations, from discovery to production lifecycle management.
- Develop innovative ML solutions, including generative AI and multi-agent approaches, and define evaluation, safety, and monitoring strategies.
- Own production deployment patterns such as containerization, CI/CD, automated testing, model registries, governance, monitoring, and rollback.
- Architect and deploy scalable ML and LLM services integrated with strategic platforms via APIs, batch, streaming, and event-driven patterns.
- Partner with cross-functional teams to influence roadmaps, align on requirements, and deliver data-led transformations.
- Establish reusable ML capabilities including feature engineering, evaluation harnesses, prompt tooling, agent frameworks, orchestration, and memory management.
- Provide technical leadership and mentorship through code reviews, design reviews, and best practices.
- Communicate technical concepts to stakeholders, translating model outputs into decisions and operational plans.
- Maintain documentation for approaches, model cards, runbooks, and operational procedures.
Requirements
- Master's degree in a quantitative field or equivalent practical experience.
- Deep understanding of machine learning fundamentals and applied data analysis.
- Experience designing rigorous evaluation and measurement in real-world settings.
- Experience deploying and operating ML models in production at scale, including monitoring and continuous improvement.
- Strong Python software engineering skills with modular design, testing, and debugging.
- Working knowledge of MLOps and LLMOps, including training/serving patterns, architectures, feature stores, and data processing.
- Ability to design intrinsic and extrinsic evaluations aligned with business goals.
- Experience in regulated environments with awareness of model risk, controls, privacy, and security.
- Strong stakeholder management and teamwork skills.