Staff Machine Learning Scientist, Financial Crime

monzoreferrals; Deutschland; Hybrid; EUR 163.000 - 204.000; Vollzeit

Benefits dieser Stelle

Equity

Hybrid work

London meetups (ad hoc)

Flexible hours

£1,000 learning budget

Visa sponsorship

Relocation support

Zusammenfassung

monzoreferrals seeks a senior individual-contributor to steer the technical direction of ML-driven financial crime and fraud prevention at scale. You will lead a cross-functional data organisation of 25+ practitioners across analytics engineering, data analysis, and ML to evolve real-time detection systems that protect customers while cutting costs.

Responsibilities include architecting a scalable platform for fraud and risk detection, delivering real-time ML models, and mentoring teams.

Qualifikationen

• 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.

Aufgaben

• Architect and advance a scalable, extensible detection platform for fraud and risk.
• Design and ship real-time ML models using DL, graph-based and sequence-based approaches for production.
• Lead end-to-end solution development across the financial crime space.
• Advise senior stakeholders and contribute to long-term fraud prevention strategy.
• Mentor ML practitioners and raise technical standards.
• Partner with MLOps to evolve tooling for rapid iteration and full model lifecycle.

Kenntnisse

Senior leadership

Python

SQL

Go

Deep learning

Graph neural networks

Transformers

MLOps collaboration

Problem solving

Jobbeschreibung

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.