[All remote jobs](https://jobicy.com/jobs.md)[![Monzo logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/06/5b4c0ec3-221.jpg)](https://jobicy.com/company/monzo.md)[Monzo](https://jobicy.com/company/monzo.md) # Senior Machine Learning Manager, Borrowing Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/monzo.md)ShareRemote from[UK](https://jobicy.com/job-region/uk.md)SalaryUndisclosedDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceSeniorPublished27 Oct 2026Apply before26 Nov 2026Listing views51Application actions2Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Monzo is seeking a senior machine learning leader to own the Borrowing ML roadmap across UK lending growth, new customer segments, and future European expansion. The role leads and develops senior ML scientists while improving production underwriting, pricing, credit limits, and lifecycle decisioning models. It requires deep credit-risk expertise, strong knowledge of machine learning and statistics, and the ability to deliver within a regulated financial environment. The manager will partner closely with Product, Engineering, Credit Strategy, Model Validation, Data, and Platform teams to build scalable credit-decisioning capabilities. The position offers a fully remote option for candidates based in the UK, with occasional travel to London. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThis is a highly senior leadership position combining advanced ML, regulated credit-risk decisioning, cross-functional strategy, and people management. Success requires both technical depth and the ability to deliver commercially meaningful models across multiple lending products and markets. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$157,000US market range (per year)$135k–$190k0$209.00k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. The employer discloses a £138,000–£176,000 salary range. The annual cadence is inferred from the compensation magnitude, senior management level, UK location, and standard full-time professional pay conventions; the posting does not explicitly state a pay period. Estimated UK market compensation for a senior credit-risk ML manager is £135,000–£190,000 per year, excluding equity, incentives, and benefits. ## Core skills Skills and capabilities most closely associated with this opportunity. [Machine Learning](https://jobicy.com/jobs?search_keywords=Machine%20Learning.md)[Credit Risk Modelling](https://jobicy.com/jobs?search_keywords=Credit%20Risk%20Modelling.md)[Underwriting](https://jobicy.com/jobs?search_keywords=Underwriting.md)[Statistical Modelling](https://jobicy.com/jobs?search_keywords=Statistical%20Modelling.md)[Lending Decisioning](https://jobicy.com/jobs?search_keywords=Lending%20Decisioning.md)[Model Deployment](https://jobicy.com/jobs?search_keywords=Model%20Deployment.md)[Feature Engineering](https://jobicy.com/jobs?search_keywords=Feature%20Engineering.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[BigQuery](https://jobicy.com/jobs?search_keywords=BigQuery.md)[Google Cloud Platform](https://jobicy.com/jobs?search_keywords=Google%20Cloud%20Platform.md)[Open Banking Data](https://jobicy.com/jobs?search_keywords=Open%20Banking%20Data.md)[Model Governance](https://jobicy.com/jobs?search_keywords=Model%20Governance.md)[People Leadership](https://jobicy.com/jobs?search_keywords=People%20Leadership.md)[Cross-functional Leadership](https://jobicy.com/jobs?search_keywords=Cross-functional%20Leadership.md) Sample interview questionsHow would you set a machine-learning roadmap for a borrowing portfolio that must support both UK growth and expansion into new European markets?I would begin by aligning the roadmap to measurable portfolio outcomes, such as approval quality, risk-adjusted margin, loss rates, customer experience, and model-development cycle time. I would identify reusable decisioning components, data gaps, regulatory constraints, and local-market differences, then prioritize initiatives through expected impact, feasibility, and governance readiness. The roadmap would balance immediate model improvements with investments in portable features, monitoring, platform capabilities, and validation processes. Describe how you would improve an existing underwriting model while ensuring robust governance in a regulated environment. I would first establish a clear baseline using out-of-time validation, segment analysis, calibration checks, fairness assessments, and business performance metrics. Proposed improvements would be tested through controlled development, documented feature and model rationale, independent validation, and defined approval workflows. After launch, I would use monitoring for drift, stability, discrimination, calibration, and operational outcomes, with clear thresholds and rollback procedures. What considerations are important when using Open Banking and transaction data for credit decisioning? The data must have clear customer consent, reliable lineage, appropriate retention controls, and consistent availability across customer groups. I would assess predictive value alongside bias, missingness, explainability, privacy, and operational resilience. Features should be designed to remain interpretable and robust to changes in data coverage, customer behavior, and third-party data providers. How do you lead senior ML scientists while maintaining high technical standards and delivery momentum? I set a shared strategy with explicit outcomes, decision rights, and quality expectations, then give experienced scientists ownership of meaningful problem areas. I use regular technical reviews, coaching, and transparent prioritization to remove blockers without micromanaging. I also create an environment where experimentation is encouraged but decisions are grounded in evidence, customer impact, and risk controls. How would you work with Product, Credit Strategy, Engineering, and Model Validation when priorities conflict? I would make the trade-offs explicit using a common framework covering customer value, financial impact, model risk, regulatory requirements, engineering effort, and time to learn. I would seek early alignment on success metrics and non-negotiable controls, while presenting options rather than escalating vague disagreements. Where uncertainty remains, I would propose a staged experiment or limited rollout that generates evidence quickly and safely. Opportunity details ## About this 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](https://monzo.com/current-account/personal) and [business](https://monzo.com/business-banking) bank accounts, we offer [joint accounts](https://monzo.com/current-account/joint-account), [accounts for 16-17 year olds](https://monzo.com/current-account/16-17), a [free kids account](https://monzo.com/current-account/under-16s) and [credit cards](https://monzo.com/flex) in the UK, with more exciting things to come beyond. Our UK customers can also [save](https://monzo.com/savings), [invest](https://monzo.com/investments)and [combine their pensions](https://monzo.com/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 | 💰 £138,000 to £176,000 + Incentive Awards tied to your performance +[benefits](https://monzo.com/careers#benefits) |[Hear from the team](https://www.youtube.com/watch?v=U62l09nB0M4) ✨ ### About the role Borrowing is one of Monzo’s most important growth engines. We’ve built strong scale in the UK, and the next phase is even more ambitious: grow in the UK, expand into new customer segments, and build the underwriting capability needed to scale into Europe. We’re looking for a senior ML leader to help shape that next phase. You’ll lead a high-calibre team of ML scientists and help define Borrowing’s ML roadmap across UK growth, new customer segments, and EU expansion. You’ll work across underwriting, pricing, limits, and lifecycle decisioning — improving today’s models while helping build the next generation of capability for Monzo. This role combines near-term commercial impact with long-term strategic advantage: * improving performance across our range of products * unlocking new data and faster model development * helping Monzo build underwriting that scales into new markets * shaping how advanced ML and AI can create real value in Borrowing You’ll partner closely with leaders across Credit Strategy, Product, Engineering, Model Validation, Data, and Platform, and influence decisions that shape both business performance and the long-term architecture of credit decisioning at Monzo. ### What you’ll own * Borrowing ML roadmaps across UK optimisation, new customer segments, and EU expansion * Faster model improvement through new data and feature engineering * How Monzo uses Open Banking, transaction intelligence, and external data in credit decisioning * More portable underwriting capabilities across products, customer segments, and markets * Hiring, leading, and developing a high-performing ML team * Raising the bar on model quality, governance, tooling, and speed of iteration ### What you’ll be working on Examples of the challenges you could help lead: * Improving underwriting models across Loans, Flex, and Overdrafts * Expand the data we use in our models * Building better feature engineering and model shipping capability * Improving transaction intelligence for credit use cases * Developing underwriting capabilities for new customer segments and EU markets * Shaping Monzo’s approach to advanced ML and AI ### Our technology stack We rely heavily on the following tools and technologies, although we do not expect applicants to have prior experience of all of them: * Google Cloud Platform * BigQuery SQL and dbt * Python and the PyData stack * Google AI Platform * AWS * Python for ML services * Go for backend services * AI tooling for productivity * Google Workspace including Gemini * ChatGPT Enterprise * Claude Code ### You should apply if * You want to shape the next phase of credit decisioning at one of Europe’s most ambitious fintechs * You enjoy combining strategic leadership with strong technical judgment * You’re excited by the intersection of underwriting, data, product, and platform capability * You like building teams as much as building models * You care about real customer and business impact ### You must have ### Credit and machine learning * Strong knowledge of machine learning, statistics, and decisioning systems * Deep experience in credit risk and lending * Experience building, deploying, and iterating credit models in production * Experience pushing the innovation frontiers of the credit industry ### Leadership and delivery * Experience leading and developing senior ML scientists * Strong cross-functional leadership with Product, Credit Strategy, Engineering, and Validation * Track record of turning strategy into practical delivery in a regulated environment ### It would be great if you also have * Experience across more than one market * Experience with advanced ML methods and AI on structured financial data ### The interview process * 30 min Recruiter Call * 45 min Initial Call * 60 min ML technical interview * 60 min People Leadership interview * 60 min Product / Credit ML interview All interviews will be conducted through Google Meet. ### What’s in it for you 💰 £138,000 to £176,000 ➕ plus stock options & benefits ✈️ We can help you relocate to the UK ✅ We can sponsor visas 📍This role can be based in our London office with a hybrid working pattern, or fully remote within the UK with occasional travel to London. 🏡 We’ll set you up to work from home, including support for remote setups. ⏰ We offer flexible working hours and trust you to work in a way that lets you do your best work. 📚 Learning budget for books, training courses and conferences ➕ And much more — see our full list of benefits here #LI-DC2 #LI-Remote Equal opportunities for everyone Diversity and inclusion are a priority for us and we’re making sure we have lots of support for all of our people to grow at Monzo. At Monzo, we’re embracing diversity by fostering an inclusive environment for all people to do the best work of their lives with us. This is integral to our mission of making money work for everyone. You can read more in our [blog](https://monzo.com/blog/diversity-and-inclusion), 2026 [Diversity and Inclusion Report](https://monzo.com/blog/our-2026-diversity-and-inclusion-report) and 2025 [Gender Pay Gap Report.](https://monzo.com/blog/gender-pay-gap-report-2025) We’re an equal opportunity employer. All applicants will be considered for employment without attention to age, ethnicity, religion, sex, sexual orientation, gender identity, family or parental status, national origin, or veteran, neurodiversity or disability status. If you have a preferred name, please use it to apply. We don’t need full or birth names at application stage 😊 Show more [Apply now >](https://jobicy.com/jobs/157407-senior-machine-learning-manager-borrowing.md) > Annual salary information is not provided for this position. 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