Applied AI ML Data Scientist, Vice President

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 useOwn 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,…