Credit Model Validation Manager (Machine Learning & NPV Models)
Company: Monzo
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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 or Remote (UK) | £79,000 - £93,000 + Incentive Awards tied to your performance + Benefits
About the team:
Our Borrowing (lending) business is growing rapidly across both existing products and with the planned launch of new initiatives. As our lending portfolio grows, our second line of defence Credit Model Validation team is growing with it - and we’re looking for a Credit Model Validation Manager to join us!
Our Credit Model Validation team is crucial in providing independent oversight and challenge to our model development teams in the first line of defence to help Monzo grow safely and ensure that the models we use deliver good outcomes for our customers. We’re looking for someone (maybe you?!) to join our validation squad and provide expert independent validation and oversight across our suite of credit models, covering critical business disciplines such as credit strategy and impairment.
You’ll have a particular focus on models used for customer-level decisioning and defining our credit strategies - such as underwriting scorecards, origination PD models and Net Present Value (NPV) / unit economics models - across all our lending products.
Strong data and analytical skills are a must, as is deep expertise in credit risk modelling. You’ll have hands-on experience in either model validation or model development within a financial services environment, including practical experience using Python to develop, validate or analyse machine learning and statistical models. . In-depth knowledge of credit decisioning models would be a strong plus.
You’ll play a key role by…
- Leading hands-on independent validation of models used for credit decisioning within our Borrowing strategies - covering a range of machine learning, decision science and NPV models. You will be ensuring that models are fit for purpose, appropriately governed, explainable and performing as expected
- Supporting oversight and validation of broader credit risk models, including IFRS9, stress testing and economic response models
- Developing deep understanding of Monzo’s credit models, and using this to provide impactful input and challenge to model developers to improve our modelling capabilities
- Reviewing model performance monitoring to ensure our lending models perform within our risk thresholds, and providing support to our first line of defence teams when action is required
- Performing statistical analyses to independently assess model performance, including developing challenger models where appropriate to test model assumptions and strengthen validation conclusions
- Improving our Model Risk Framework, including overseeing our policies, standards and procedures to help embed best practice across the business. This means making sure we follow up-to-date regulatory guidelines, and adapt our processes to make sure they remain relevant as modelling techniques evolve
- Writing clear and impactful model validation reports, and presenting the findings and actions in relevant committees
- Partnering with colleagues in the first line of defence credit strategy and modelling teams - and wider risk teams across Monzo - to support the embedding of our model risk framework and establishing consistent validation standards across the organisation
- Embracing new and innovative technology, including leading the adoption of AI tools to enhance our model validation approach
We’d love to hear from you if…
- Have a strong background and practical experience in credit model development and/or validation - in particular with exposure to machine learning or statistical models used in credit decisioning, such as decision science scorecards, origination / underwriting PD models, and NPV / unit economics modelling
- Are data driven, with strong analytical skills and deep understanding of statistical techniques for credit and model risk. Being able to get hands on with data is critical - so strong SQL and Python skills are essential, and experience with data visualisation tools such as Tableau, Looker or similar is a plus!
- Understand how models are used in real-time decisioning and lending strategies, and how to balance commercial and risk outcomes
- Have strong written and verbal communication skills, and are able to articulate problems and solutions using clear and simple language. This is particularly important for breaking down complex issues and priorities to non-experts
- Have a track record of building and maintaining trusting relationships with stakeholders at all levels, and can provide effective and respectful challenge
- Are impact-driven, and proactive in identifying issues and opportunities to improve outcomes for both our customers and our business
- Are passionate about technology, and what we’re doing here at Monzo excites you!
Our technology stack
We rely heavily on the following tools and technologies (note we do not expect applicants to have prior experience of all them):
- Google Cloud Platform for all of our analytics infrastructure
- dbt and BigQuery SQL for our data modelling and warehousing
- Python for data science and machine learning
- Go to write our application code
- AWS for most of our backend infrastructure
The interview process:
Our interview process usually involves up to four main stages via Google Hangouts:
- An initial call with our recruiter
- A call with our Senior Model Validation Manager, the hiring manager for the role
- A role-specific call with colleagues from our credit modelling teams. We’ll share a short case study in advance, but you won’t need to submit a written response; we’ll use it to guide the discussion during the interview.
- A behavioural interview
What’s in it for you
- £79,000 - £93,000 + Incentive Awards tied to your performance benefits.
- This role can be based in our London office, but we're open to distributed working within the UK (with ad hoc meetings in London).
- ⏰ We offer flexible working hours and trust you to work enough hours to do your job well, and at times that suit you and your team.
- £1,000 learning budget each year to use on books, training courses and conferences.
- We will set you up to work from home; all employees are given MacBooks and for fully remote workers we will provide extra support for your work-from-home setup.
- ➕ Plus lots more! Read our full list of benefits.
We have some guidelines on using Artificial Intelligence (AI) to ace an application and interview at Monzo. You can read them here.
We’ll close this role we have enough applications for the next stage, so please apply as soon as you can - we don’t want you to miss out!
LinkedIn Tags: #LI-TK1
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, 2026 Diversity and Inclusion Report and 2025 Gender Pay Gap Report.
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
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