About the role
🚀 We’re on a mission to make money work for everyone. We’re waving goodbye to the complicated and confusing ways of traditional banking. After starting as a prepaid card, our product offering has grown a lot in the last 10 years in the UK. As well as personal and business bank accounts, we offer joint accounts , accounts for 16-17 year olds , a free kids account and credit cards in the UK, with more exciting things to come beyond. Our UK customers can also save , invest and combine their pensions with us. With our hot coral cards and get-paid-early feature, combined with financial education on social media and our award winning customer service, we have a long history of creating magical moments for our customers! We’re not about selling products - we want to solve problems and change lives through Monzo ❤️ 📍London/Cardiff/UK Remote | 💰 £113,200 - £153,200 + Incentive Awards tied to your performance +
Benefits ✨ Our Financial Crime and Fraud Team ⭐ Financial crime causes real harm. In Fraud, we build controls that help protect customers from scams, stolen-card fraud, account takeover and other fast-changing threats, all while keeping Monzo smooth and fair for the millions of people who use us every day. Machine Learning is central to that work. Our ML specialists build, ship and improve models that help us spot risk, make better decisions, and keep our customers safe. We work closely with Product, Risk Partners, Engineering, Data and other disciplines to turn ambiguous fraud problems into reliable controls that make a measurable difference for customers. We’re looking for a Machine Learning Manager to lead ML specialists across different levels of seniority in the Fraud group. You’ll report to the Senior ML Manager for the FinCrime Collective, and you’ll help
the team deliver high-impact ML systems while growing a healthy, inclusive and high-performing team. 🔑 You’ll play a key role by… Managing, coaching and developing a team of Machine Learning specialists, helping them do the best work of their careers through regular 1:1s, clear feedback and thoughtful development plans. Helping the Fraud group focus ML effort on the highest-impact problems, balancing customer protection, regulatory expectations, operational impact and delivery pace. Partnering with PMs, Risk Partners, Engineering Managers, Data Managers and senior ML/Data colleagues to shape roadmaps, unblock delivery and make clear trade-offs. Creating the conditions for high-quality ML delivery: reliable model development, deployment, monitoring, governance, documentation and ownership across the full model lifecycle. Holding a high bar for ML excellence in Fraud, including model performance, explainability, reproducibility, observability and safe operation in production. Making sure ML products are owned, maintained and improved over time, rather than treated as one-off projects. Building a strong team culture where people give and receive useful feedback, learn from each other, and work well with cross-functional partners. Contributing to the wider Machine Learning and Data discipline at Monzo, including hiring, improving ways of working, and sharing patterns that help other teams move faster and more safely. 🤩 We’d love to hear from you if… You’ve managed Machine Learning specialists and have delivered through others and want to move further into people management. You have strong ML judgement and can guide teams through the full model lifecycle, from problem framing and feature/model development through to deployment, monitoring, governance and iteration. You’re comfortable working in ambiguous, high-impact problem spaces where there often isn’t one “right” answer. You can build trusted relationships with Product, Risk, Engineering, Data and senior stakeholders, and you know how to bring the right people into the right conversations early. You focus on outcomes over process, and can help a team prioritise work that protects customers and moves the business forward. You know what good looks like for production ML systems, and you can raise the bar through coaching, review, standards and pragmatic decision-making. You communicate clearly with technical and non-technical audiences, especially when explaining trade-offs, risks and recommendations. You care deeply about building inclusive teams where people with different levels of experience can grow, challenge each other, and do high-quality work sustainably. Experience in fraud, financial crime, risk, regulated environments or customer protection would be a