Model Risk Manager - Empower, Influence, and Lead

InterQuest Group (UK) Limited
Exeter
9 months ago
Applications closed

Related Jobs

View all jobs

Technical Program Manager - Machine Learning - New York

Technical Program Manager - Machine Learning - New York

Machine Learning Engineer (Manager)

Machine Learning Engineer (Manager)

Machine Learning Engineer (Manager)

Manager – Data and Data Science Strategy – Emerging Data and Capabilities

Model Risk Manager – Empower, Influence, and Lead

up to £90K + excellent benefits and bonus

Hybrid/Essex (2 days in office)

Are you ready to take your expertise in risk management to the next level?Step into a pivotal role where your analytical skills, leadership, and strategic thinking will directly impact decision-making at the highest levels. As aModel Risk Manager, you will play a key role within the Enterprise Risk team, with direct exposure to senior management and theBoard of Directors.

This role offers the opportunity towork across multiple European markets, ensuring compliance withUK FCA and PRA regulations, while also navigating additional local regulatory requirements. You will be instrumental inshaping model risk strategy, enhancing the risk frameworks, anddriving real changewithin the organization.

If you're aproblem-solver with a passion for risk, analytics, and leadership, we want to hear from you!

Why Join this Bank?

High-Impact Role– Influence decisions at senior levels and work directly with executives and Board members.

Leadership & Visibility– Be a part of the Enterprise Risk team, a key second-line function, ensuring strategic oversight of critical risk areas.

International Exposure– Work across diverse European markets and develop a deep understanding of different regulatory landscapes.

Professional Growth– Expand your expertise inmodel risk management overseas and with peers in Treasury, ESG strategy, and risk frameworks.

Supportive & Inclusive Culture– Work in an environment that values diverse perspectives and fosters continuous learning and collaboration.

Key Responsibilities

Model Risk Management

  • Lead the development and maintenance ofModel Risk Management Policies and Frameworks, ensuring compliance with evolving regulations.
  • Work closely with model developers, owners, and independent validators tomonitor, improve, and approve risk models.
  • Implement cutting-edgerisk measurement and reportingtechniques, setting model risk appetites aligned with strategic objectives.
  • Challenge existing models with innovativePost-Model Adjustments, ensuring accuracy and resilience.

Model Development & Validation

  • Partner with teams todevelop robust modelsfor ICAAP Pillar 2 risks, includingcredit, operational, market, and concentration risk.
  • Drive research and innovation by designingchallenger models and stress testing methodologies.
  • Validate models with a strategic and independent mindset, ensuring high-quality risk assessments.

Enterprise Risk & Strategic Support

  • Contribute to critical risk initiatives such asICAAP, Recovery Planning, ESG strategies, and Risk Appetite Frameworks.
  • Engage with senior committees tocommunicate complex risk concepts in a clear and impactful manner.

Who You Are

?A visionary risk expert– with a Master's or PhD inStatistics, Mathematics, Econometrics, or Financial Engineering.

?A specialist in financial risk modeling– with expertise inExpected Credit Loss (IFRS9)and banking risk areas (credit, market, operational, and rate risk).

?An analytical thinker with data science skills– adept atSAS, Alteryx, Excel VBA, and leveraging data to drive strategic decisions.

?A confident communicator– with the ability to present technical insights to senior leadership in aclear and engaging manner.

?A problem-solver– who can navigate complexities, manage competing priorities, and drive pragmatic solutions.

Bonus Points If You Have…

Experience withIRB approach development

Regulatory experience – working with or for financial regulators

Ready to Make an Impact?

Call us and discuss how you can be part of this team that values expertise, innovation, and leadership. If you're ready to elevate your career in risk management and shape the future of financial resilience,apply now!

InterQuest Group is acting as an employment agency for this vacancy. InterQuest Group is an equal opportunities employer and we welcome applications from all suitably qualified persons regardless of age, disability, gender, religion/belief, race, marriage, civil partnership, pregnancy, maternity, sex or sexual orientation. Please make us aware if you require any reasonable adjustments throughout the recruitment process.


JBRP1_UKTJ

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.