Table of Contents
Model Risk Manager (UK Bank) – Revolut (Glasgow, UK)
Job Overview
Revolut is seeking a driven, data-focused Model Risk Manager to join its UK Bank Risk team in Glasgow, United Kingdom (with hybrid/remote flexibility).
This role sits at the intersection of quantitative risk, data science, and governance. The primary purpose is to oversee, validate, and manage model risk across the UK Bank’s expanding product portfolio—with a strong focus on advanced Machine Learning (ML), Artificial Intelligence (AI), Anti-Money Laundering/Anti-Terrorist Financing (AML/ATF), fraud detection, and computer vision models.
Key responsibilities include:
- Model Validation & Oversight: Evaluating, stress-testing, and validating complex quantitative and machine learning models across the full model lifecycle (development, implementation, monitoring).
- Executive & Board Reporting: Translating intricate model outputs, assumptions, and limitations into actionable technical insights for senior management and the Board Risk & Compliance Committee.
- Risk Mitigation & Recommendations: Advising model developers on methodology improvements, ensuring all models operate strictly within Revolut’s risk appetite parameters.
- Methodology Enhancement: Challenging existing modeling frameworks and championing continuous improvement in data analytics, risk controls, and governance.
- Cross-Functional Engagement: Collaborating with teams across Product, App Technology, Treasury, Finance, and Financial Crime to embed model risk standards early in new business initiatives.
About Us
Revolut is a global financial super-app with over 75 million customers worldwide and 13,000+ employees globally. Founded in 2015, Revolut offers spending, savings, investment, currency exchange, and credit products. Certified as a Great Place to Work™, Revolut UK operates under its UK Banking entity, delivering secure, tech-driven retail and commercial banking solutions while pushing the boundaries of AI/ML integration in finance.
Key Requirements & Qualifications
- Experience: 4+ years of hands-on experience developing or validating quantitative risk or data science models (e.g., Financial Crime, Machine Learning, Market Risk, Liquidity, Capital, or Treasury models).
- Technical Stack: Strong proficiency in Python and SQL for data analysis, model audit, and statistical testing.
- Quantitative Background: In-depth understanding of time series analysis, probability distributions, regression modeling, and Monte Carlo simulations.
- Domain Expertise: Familiarity with modern ML/AI applications, particularly in computer vision, fraud prevention, or financial crime detection (AML/ATF).
- Stakeholder & Soft Skills: Ability to operate autonomously, work effectively under high pressure, and clearly communicate complex technical concepts to non-quantitative executives.
Role Benefits & Perks
- Flexible & Remote Work: Revolut’s pioneer hybrid/remote policy allows employees to balance working from home and collaborating in local hubs.
- Global Mobility & Career Progression: Rapid internal progression opportunities within a fast-scaling global fintech unicorn operating across dozens of international markets.
- Diversity & Inclusion Focus: Part of an inclusive, multicultural workforce supported by Revolut’s formal D&I Framework.
- Equity & Incentives: Competitive compensation packages with potential performance incentives and stock options (subject to offer terms).
Salary Insight
- Estimated Base Salary Range: Based on UK fintech and quantitative risk benchmarks in Glasgow/UK regional hubs, compensation for a Model Risk Manager at Revolut typically ranges between £65,000 and £85,000 annually, plus performance bonuses, equity options, and benefits.
Career Guide Tips for Model Risk Managers in Fintech
- Understand ML Explainability (XAI): Unlike traditional linear credit models, AI/ML models require advanced explainability techniques (e.g., SHAP, LIME) to meet regulatory model risk management (MRM) standards like the PRA’s SS1/23 guidelines.
- Bridge Data Science and Governance: Top MRM professionals act as a bridge—they possess the coding fluency of a Data Scientist alongside the regulatory rigor of a Bank Risk Director.
- Stay Ahead of FinCrime Trends: Learn how financial crime networks attempt to bypass automated transaction monitoring and computer vision KYC checks to better challenge fraud models.
How to Prepare a CV for This Position
- Highlight Technical & Quantitative Tools: Explicitly feature Python packages (e.g., NumPy, pandas, scikit-learn, Statsmodels) and SQL database experience near the top of your resume.
- Specify Model Types: Explicitly list the exact models you have built or validated (e.g., “Validated XGBoost Fraud Detection models,” “Reviewed ICAAP/IRB Capital models,” “Audited Computer Vision KYC frameworks”).
- Demonstrate Impact on Governance: Showcase experience presenting to Risk Committees, drafting validation reports, or implementing MRM frameworks.
How to Prepare for the Interview
- Brush Up on Coding & Probability: Expect live technical screening or practical coding exercises in Python and SQL focusing on statistical testing, data manipulation, and model metrics (AUC-ROC, Precision/Recall, Gini).
- Prepare Case Studies using STAR: Be ready to walk through a complex model validation project where you identified a critical flaw, challenged the model developers, and successfully influenced executive decision-making.
- Study Regulatory MRM Standards: Review the UK Prudential Regulation Authority (PRA) Supervisory Statement SS1/23 on Model Risk Management for Banks to discuss governance best practices effortlessly.
Top 3 Interview Questions & How to Answer Them
1. “How do you approach validating a black-box Machine Learning model (e.g., gradient boosting or deep learning) used for fraud detection?”
- Key Focus: Detail how you assess conceptual soundness, evaluate training data bias/leakage, apply explainability techniques (SHAP/LIME), test for model drift, and establish ongoing performance monitoring thresholds.
2. “Walk us through a situation where a developer strongly disagreed with your model validation finding. How did you resolve it?”
- Key Focus: Highlight your objective, evidence-based approach. Show how you used data-driven evidence and risk materiality logic to reach a consensus without compromising on risk appetite or bank safety.
3. “How do you test the assumptions and limitations of a Monte Carlo simulation or time-series model under extreme market stress?”
- Key Focus: Discuss stress testing methodologies, sensitivity analysis, tail-risk evaluation, and how you clearly communicate these boundary conditions to executive committees.
How to Apply
Apply exclusively through official Revolut career channels:
- Visit the official Revolut Careers Portal
- Search for the Model Risk Manager – Glasgow / UK Bank listing.
- Submit your CV and details directly.
Candidate Safety Notice: Revolut never uses third-party platforms for recruitment payments, nor will they ask for personal financial information during hiring. Ensure all official correspondence comes from an @revolut.com email address.
Disclaimer
This job summary is an independent document created for informational, educational, and career guidance purposes only. It is not an official offer of employment or direct advertisement from Revolut Ltd. Job duties, requirements, salary estimates, and benefits are subject to change by the employer.