Lead Machine Learning Scientist, FinCrime

About the role

You will architect and implement advanced machine learning systems that detect financial crime while preserving a seamless customer experience for millions of users. This role owns the end to end lifecycle of high impact models, from problem framing and data strategy through to robust production deployment and continuous performance improvement. You will partner closely with product, compliance, and engineering teams to turn ambiguous business challenges into well defined technical solutions that directly protect customers and reduce operational risk. You will act as a technical thought leader within the FinCrime domain, setting standards for model quality, experimentation rigor, and responsible AI practices across the broader data organization. The position demands curiosity, ownership, and a bias for shipping in a fast moving environment where priorities evolve with emerging threats. You will translate complex analytical findings into clear narratives that align technical tradeoffs with business objectives and customer safety.

Key facts

What you'll do

Define and scope high impact machine learning initiatives in collaboration with product managers, data scientists, and compliance stakeholders to tackle the most critical financial crime problems.

Design, develop, and deploy advanced real time machine learning models that analyze billions of rows of data to detect suspicious behaviour while minimizing false positives and operational cost.

Adapt rapidly to evolving fraud and financial crime trends, ensuring detection systems remain accurate, performant, and resilient over time through rigorous experimentation and monitoring.

Lead the technical design and implementation of models, selecting appropriate algorithms, features, and evaluation strategies that meet strict regulatory and business requirements.

Work cross functionally with product, engineering, and data teams to translate complex analytical insights into production grade solutions that deliver measurable customer and company benefit.

Own the end to end machine learning lifecycle, including data strategy, model development, validation, monitoring, and continuous improvement in a cloud native data platform.

Establish and uphold technical standards for model quality, experimentation, and responsible AI practices across the FinCrime and broader data science community at Monzo.

Communicate findings, tradeoffs, and recommendations clearly to both technical and non technical audiences, aligning solutions with customer needs and regulatory constraints.

Champion innovation and safe experimentation, encouraging evidence based exploration of new techniques while ensuring robust evaluation and risk management.

Act as a technical leader and mentor, elevating the capabilities of cross functional partners and contributing to the long term technical roadmap for financial crime defenses.

Support the delivery of operational tooling and dashboards that enable stakeholders to monitor model performance, investigate alerts, and make timely decisions.

Collaborate closely with compliance and operations to ensure that machine learning solutions remain explainable, auditable, and aligned with evolving regulatory expectations.

Identify opportunities to reduce manual effort and automate decisioning through machine learning, improving efficiency and consistency in fraud detection workflows.

Drive best in class performance on key metrics such as detection accuracy, precision, recall, and operational efficiency through disciplined experimentation and analysis.

Contribute to a culture of learning and knowledge sharing, documenting approaches, lessons learned, and best practices to benefit the wider organization.

Requirements

You are eligible and must meet the following hard bars as a condition of employment.

You have the right to work in the UK and will be eligible for any required visa sponsorship depending on your location and circumstances.

You hold a PhD, or Masters with significant research experience, in a relevant quantitative field such as computer science, statistics, mathematics, or a related discipline.

You possess deep expertise in machine learning methods, including supervised learning, unsupervised learning, and modern approaches to anomaly and fraud detection.

You have extensive experience developing, deploying, and monitoring machine learning models in production at scale, handling large datasets and complex feature spaces.

You demonstrate a strong track record of building and maintaining high performance, low latency systems that process billions of rows of data in real time environments.

You have a proven ability to work effectively in cross functional agile teams, collaborating with product managers, engineers, and compliance specialists.

You are comfortable interpreting complex business problems and translating them into rigorous technical solutions with clear success metrics.

You hold a strong commitment to ethical AI, data privacy, and regulatory compliance, with experience working in highly regulated financial environments.

Nice to have

Proven experience in financial crime, fraud detection, transaction monitoring, or other regulated domain applications.

Experience building and operating machine learning systems on modern cloud platforms and data infrastructure.

Knowledge of streaming data processing frameworks and real time analytics architectures.

Familiarity with explainability and model monitoring techniques that support compliance and audit requirements.

Experience mentoring data scientists or machine learning engineers and contributing to technical standards.

Practical notes

This is a full time role with standard UK working hours.

Travel is not required as the position can be fulfilled remotely within the UK.

Visa sponsorship may be considered for eligible candidates based on location and role requirements.

Applications will be reviewed on a rolling basis until the role is filled.