Senior Machine Learning Scientist, Borrowing
MonzoCardiff, London or Remote (UK)£86k – £105kPosted 21 March 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/Cardiff/UK Remote | 💰 £86,000-105,000 + 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. Through magically simple products and actionable insights, we put our customers in control of their finance. Our products are different by design, and reliable at our core.
Our range of borrowing products are critical to Monzo’s mission. Not only do they serve important needs of our customers, they are also a key revenue driver to support Monzo keep delivering great products and experience. We have seen stellar growth and deep engagement with millions of borrowers, supported by effective and efficient credit risk management. Our product portfolios are still expanding fast, from personal to business credit, and markets beyond UK. We are looking for bright, passionate and creative individuals to further accelerate our growth.
About the role
The mission of Borrowing ML 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 working alongside a team of very experienced and highly efficient ML Scientists, with well established toolings for the fully lifecycle of ML models. Each of you owns multiple ML applications end-to-end, from experiment design and data curation, to deployment and monitoring. You will be empowered to innovate in the data, methodologies and toolings, so we can build better models easier and faster.
You will have exposure to all Borrowing products and applications, with autonomy to decide what are the most impactful topics to work on, and how to deliver them. You will work closely with our Credit Strategy Managers, Model Validation Analysts, Backend Engineers, and Product Managers, to fit your model development into the product roadmap. You are also empowered to think big about the business, market and customers, to influence our product and credit strategy beyond just the world of models.
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 Vertex 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 enjoy a high degree of autonomy and thrive in a fast-paced environment
You are keen to grow your knowledge in both business and technology
📌 You must have:
Excellent SQL and Python skills with good understanding of best practices in softwa ... (truncated, view full listing at source)
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