This role is based in the United Kingdom and as such all normal working days must be carried out in the United Kingdom.
Join us as a Data Science & AI Lead
- In this key role, you’ll lead Data Science and AI capabilities for the Economic Crime Hub, designing, building, and scaling production-grade models, AI systems, and automated decisioning solutions
- You’ll own the end-to-end lifecycle of models and AI agents, ensuring decisions are executed safely, consistently, and effectively at scale
- Make an impact by building a high-performing Data Science and Machine Learning (ML) Engineering capability that drives innovation and business outcomes through AI and advanced analytics
As a Data Science & AI Lead, you'll own the end-to-end Data Science and AI agenda, delivering scalable models, AI systems, and agentic workflows embedded within decision-making across the Hub. You’ll oversee the design, build, and deployment of models and AI solutions into production environments, ensuring they deliver reliable, high-performing outcomes that support business objectives at scale.
You’ll also establish and enforce standards for model development, Machine Learning Operations (MLOps), monitoring, and lifecycle management, while partnering closely with Analytics, Product, and Technology teams to translate decision strategies and decision intent into automated, production-grade solutions, ensuring technical feasibility, scalability, and integrity. Moreover, you’ll build and lead a multi-disciplinary Data Science capability, encompassing Data Scientists, ML Engineers, and MLOps specialists, ensuring strong technical depth, continuous development, and clear progression across the function.
In addition, you’ll be:
- Acting as the single-threaded owner for the automation of decision-making, translating analytical logic and decision strategies into production-grade models and AI systems across the full lifecycle
- Overseeing model performance, including accuracy, stability, drift, reliability, and the real-world effectiveness of decision-making in production environments
- Embedding controls, validation, monitoring, and safeguards into models and AI systems by design
- Advancing AI and ML capabilities, including agentic AI, Large Language Model (LLM) use cases, MLOps, and automation frameworks, while increasing automation without adding risk or control debt
- Designing scalable architectures, standardised pipelines, and reusable model components to enable efficient and repeatable delivery
- Building a high-performing Data Science, ML Engineering, and MLOps function, driving technical excellence, innovation, and career development
- Ensuring models are explainable, traceable, auditable, and compliant with governance frameworks, while partnering with Model Risk on technical implementation and model behaviour
We're looking for someone with extensive experience leading engineering-grade Data Science and AI capabilities, delivering scalable, automated decisioning systems. You must have a proven track record of designing and deploying AI-driven solutions, leveraging advanced modelling techniques and emerging approaches such as LLMs and agentic workflows.
Moreover, you'll need to bring technical expertise to lead the delivery of production-grade AI solutions and guide their adoption within a complex, regulated environment. You should also have the ability to provide technical leadership, foster high-performing teams, and drive innovation while maintaining strong governance and risk management practices.
Experience working within financial crime or fraud environments would be preferred
In addition, you’ll need to demonstrate:
- Deep expertise in model development and lifecycle management, including building, deploying, monitoring, retraining, and retiring models in production environments
- Strong knowledge of MLOps, model monitoring, and production systems, ensuring the reliability, performance, and operational effectiveness of models and automated decisioning
- Experience working within regulated environments, with a strong understanding of model governance, explainability, auditability, and data risk management
- Success in building and scaling high-performing teams across Data Science, ML Engineering, and MLOps functions
