Machine Learning Manager, Borrowing

Monzo
Cardiff, London or Remote (UK)£100k – £160kPosted 3 April 2026

Job Description

🚀 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 | UK remote | 💰 £100,000 to £160,000 (dependant on level) + Benefits | Hear from the team About us: At Monzo we want to make money work for everyone. We care deeply about our 15+ million customers. Our products are different by design: through magically simple products and actionable insights, we put our customers in control of their finance. Our range of borrowing products serve the important needs of our customers and are critical to Monzo’s mission. We have seen stellar growth and deep engagement with millions of borrowers, supported by effective credit risk and machine learning expertise. Our product portfolios are still expanding fast, from personal to business credit, and to markets beyond the UK. We are looking for bright, passionate and creative individuals to further accelerate our growth. About the role: The mission of Borrowing Machine Learning Scientists is to improve the customer and business outcomes through better automated decisioning, using Machine Learning and statistical modelling. We have a primary focus in credit risk modelling, with our expertise also applied to predict and optimise utilisation, pricing, collection and marketing. You will be taking a hands-on technical leadership role, managing a small team of Senior ML Scientists. With in-depth knowledge and experience in the credit industry, you’ll drive innovations by identifying new opportunities of data and ML applications, and delivering business values across multiple Borrowing products. You’ll have a close relationship with leaders across Credit Strategy, Products, Model Validation and Engineers, with whom you will drive and influence strategic decisions and product roadmaps. You’ll also have the opportunities to work with external data suppliers and industry peers, to push the innovation frontiers for the credit industry. With excellent technical skills, you’ll also spearhead the continuous development of our toolings, methodologies, and processes, to empower the team to , ,/ build better models easier and faster. You’ll serve as the champion for the quality and efficiency of model development, and ensure safe and scalable growth of our model portfolio. You’ll also have plenty opportunities to work with other modelling teams across Monzo, to collaborate on the best practices and latest technologies. Our technology stack We rely heavily on the following tools and technologies (although we do not expect applicants to have prior experience of all them): Google Cloud Platform for all of our analytics usages BigQuery SQL and dbt for our data modelling and warehousing PyData stack for model development and offline deployment Google AI platform for cloud computing AWS for backend infrastructure Python for ML model microservices Go lang for most other microservices AI toolings for productivity (an evolving list) Google suites including access to Gemini ChatGPT enterprise Claude code You should apply if: You are result oriented and motivated by the impact on our customers and business You thrive in a fast-paced environment and comfortable with frequent context switching You enjoy both the s ... (truncated, view full listing at source)
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