Monzo - Credit Model Validation Manager (Machine Learning & NPV Models)

• 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 ongoing 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