Staff Machine Learning Scientist, Financial Crime

Role overview

A senior individual-contributor opportunity leading the technical direction of machine-learning based financial crime and fraud prevention at scale. The role sits within a cross-functional data organisation of 25+ practitioners spanning analytics engineering, data analysis, and ML, with a mandate to evolve real-time detection systems that protect customers while reducing operational cost.

Responsibilities

• Architect and advance a scalable, extensible, automated detection platform covering fraud, transaction monitoring, and customer risk assessment.
• Design and ship real-time ML models using deep learning, graph-based, and sequence-based approaches for production environments.
• Identify the highest-impact opportunities across the financial crime collective and lead solution development end-to-end.
• Advise senior business stakeholders and contribute to long-term strategy for fraud and financial-crime prevention.
• Mentor ML practitioners and raise the technical bar across the discipline through example and knowledge sharing.
• Partner with MLOps to evolve tooling that supports rapid iteration and optimisation of the full model lifecycle.

Requirements

• Several years of senior experience leading the technical work of ML teams, with measurable production impact.
• Hands‑on background designing and deploying advanced ML systems in financial crime, fraud, security, or trust and safety.
• Deep expertise in deep learning, graph neural networks, transformers, or comparable architectures for real‑time detection.
• Strong, daily production-level proficiency in Python and SQL, with willingness to learn Go for backend microservices.
• A self-starter mindset that proactively surfaces and tackles the most impactful problems.
• Comfort navigating ambiguity and helping stakeholders and teammates resolve it.

Benefits and work setup

• Salary range £140,000–£175,000 plus equity and benefits.
• Hybrid working from a London office or fully remote within the UK, with ad hoc London meetups.
• Flexible hours, part-time considered, and a £1,000 annual learning budget for books, courses, and conferences.
• Visa sponsorship and relocation support available.