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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

Dublin | €124,000 - €167,000 + Incentive Awards tied to your performance + benefits | Hear from the team
• &star; Our Risk team

The Head of Model Risk Oversight is a key Second Line of Defence (2LOD) leadership role responsible for establishing, scaling and managing the Model Risk Management (MRM) process across Monzo Bank Europe (MBEU).

As automated systems, advanced statistical models and Artificial Intelligence (AI) increasingly drive business activities and decision-making, this role ensures that all models supporting MBEU day to day operations including credit scoring, fraud and financial crime transaction monitoring, capital and liquidity stress testing, and AI Act-regulated use cases are mathematically sound, secure, compliant, and aligned with MBEU's risk appetite and regulatory expectations.

This role reports to the Director of Credit Risk, MBEU.

You'll play a key role by...

Model Risk Management (MRM) & Governance

Defining, implementing and maintaining the EU-wide Model Risk Management (MRM) Framework and associated validation standards. This includes developing and implementing the model risk appetite, model risk assessment process, model risk reporting and ongoing model reviews and validation schedules.
Serving as the primary driver and coordinator for the Model Risk Oversight Committee (MROC), ensuring material risk metrics, model validations and escalations are effectively reported to the Executive Risk Committee (ERC) and the Board Risk Committee (BRC).
Establishing and maintaining a centralised suite of model validation standards including those used for performance monitoring.
Maintaining MBEU Model Inventory including AI Use Case registries, ensuring appropriate tiering classification of material and high-risk models in alignment with the EU AI Act.
Managing the local embedding of the group-wide model risk governance frameworks, ensuring appropriate alignment and clear escalation protocols between global frameworks and subsidiary-specific policy standards.

Independent Model Validation & Oversight

Leading the technical 2LOD validation of all material and high-risk models prior to deployment, evaluating mathematical logic, data lineage, conceptual soundness, stability, performance and potential algorithmic bias.
Providing 2LOD independent oversight and technical validation across key risk domains including:

Credit Risk covering credit scoring, automated underwriting decision engines, customer affordability and IFRS 9 loan loss provisioning models.
Fraud & Financial Crime Risk covering digital onboarding risk assessment scorecards and real-time fraud/transaction monitoring models.
Financial Risk covering financial planning models, ICAAP/ILAAP stress testing, Pillar 2 economic capital models, macroeconomic forecasting, Interest Rate Risk in the Banking Book (IRRBB) models.

Establishing ongoing monitoring standards, including back-testing, benchmarking, and sensitivity analysis, to detect model drift or performance degradation.
Issuing formal 2LOD Opinions on model changes, and track validation findings and action closures for regular reporting to the Model Risk Oversight Committee (MROC).

Advanced Modelling Technologies and AI Governance

Developing and operationalising independent 2LoD validation protocols for advanced Machine Learning (ML) methodologies and automated decisioning systems running on production platforms.
Establishing targeted risk oversight frameworks and registration protocols for Generative AI and Large Language Model (LLM) use cases deployed across bank operations.
Directing technical validation activities to meet compliance with evolving European AI Act legislation and Monzo's internal Artificial Intelligence Standards.

Regulatory Compliance

Reviewing all methodologies, modeling techniques and validation approaches to ensure continuous alignment with local and European regulatory expectations.
Leading the 2LOD assessment of evolving regulatory requirements with a specific focus on ECB and EBA Guidelines (eg, Loan Origination and Monitoring), IFRS 9 accounting standards, GDPR data privacy mandates and European consumer credit directives.

Committee Governance and Independent Challenge

Serving as the Secretariat function for the MBEU Model Risk Oversight Committee shaping technical agendas, tracking validation actions, and presenting validation reports and 2LOD opinions.
Managing formal reporting lines and ensuring the immediate escalation of material Model Risk Appetite breaches or validation failures to the Executive Risk Committee (ERC) and Board Risk Committee (BRC).
Providing clear, objective and evidence-based technical challenge to first-line (1LOD) model developers, model owners and accountable executive stakeholders throughout the validation lifecycle.

Leadership and Stakeholder Management

Establishing and scaling the 2LOD model risk oversight function, managing resourcing and budget and customising risk governance processes for a newly authorised entity.
Optimising the 2LOD validation workflow through the evaluation and adoption of developer productivity utilities and generative or agentic AI co-working tools.
Acting as the principal point of contact for external regulators (e.g., Central Bank of Ireland), internal audit and external reviewers regarding model risk profile and validation backlogs.
Challenging 1LOD quantitative development teams constructively, balancing business velocity and commercial goals with rigorous risk boundaries.

We'd love to hear from you if...

You have a post-graduate degree (MSc or PhD) in a highly quantitative discipline such as Statistics, Mathematics, Financial Engineering, Data Science or Physics.
You bring extensive experience in quantitative Model Risk Management within a tier-1 bank, rapid-growth challenger bank, or premier financial regulatory consulting practice.
You possess advanced quantitative knowledge across retail credit risk scoring (PD, LGD, EAD), IFRS9 impairment modeling, Economic Capital, ICAAP and ILAAP, macroeconomic stress testing, and fraud/financial crime modeling techniques.
You have a deep understanding of European prudential regulations, ECB internal model guidelines, ECB Guide to ICAAP and ILAAP, EBA standards, Consumer Credit Directive (CCD2), GDPR data privacy mandates, and the European AI Act.
You are highly tech-fluent, featuring hands-on proficiency in SQL and Python, with an eagerness to deploy generative or agentic AI tools to optimize risk workflows.
You demonstrate the capability to deliver clear, structured, and objective technical challenge to first-line analytics, data science, and engineering squads.
You possess a strong track record in designing, scaling, or managing comprehensive model risk governance frameworks from scratch under data-sparse conditions.
You are capable of translating complex econometric, statistical and machine learning concepts into clear, actionable, risk-oriented insights for the senior management and the board.

What's in it for you

A salary range of €124,000 - €167,000 + Incentive Awards tied to your performance

This role will have a hybrid working model, based in our Dublin office 2 - 3 days a week. You will also travel to Monzo's head office in London from time to time

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

Annual Leave - 34 days including public holidays (24 holiday days + 10 public holidays)

€1,200 learning budget each year to use on books, training courses and conferences

Private healthcare scheme

Pension scheme: the minimum contribution is 4% and Monzo matches any additional contributions that you make up to a maximum of 6%

Wellbeing benefits: financial education, women's and men's health support, mental health benefits, including coaching and counselling

We'll help you relocate to Ireland

The application journey has 3 key steps

An introductory call with a member of the Monzo Recruitment team
An initial call with the hiring manager
A series of role specific, behavioural & leadership principles interviews

This process should take around 3-4 weeks - your schedule is really important to us, so we promise to be as flexible as possible! We have some guidelines on using Artificial Intelligence (AI) to ace an application and interview at Monzo. You can read them here.

We'll only close this role once we have enough applications for the next stage. Please submit your application as soon as possible to make sure you don't miss out.

The role you are applying for is a Controlled Function within the meaning of the Central Bank Reform Act, 2010. For this role, appointment will be conditional on Monzo being fully satisfied that the appointee meets the requirements as set out in the Fitness and Probity Standards issued by the Central Bank of Ireland. As a CF, the role holder will be subject to Conduct Standards under the Central Bank of Ireland Individual Accountability Framework (IAF) and will be required to take reasonable steps to ensure that the relevant conduct standards are met.

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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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