Fraud Model Governance Lead

Virgin Money
2 months ago
Applications closed

Related Jobs

View all jobs

Lead credit risk data scientist

Principal Data Science Consultant - Financial Services Expertise

Principal Data Science Consultant - Financial Services Expertise

eDiscovery Project Managers (all levels)

Counter Fraud Data Scientist

Senior Pricing Data Scientist

Business Unit:COO, Fraud
Salary Range£60,000 - £75,000 DOE + red-hot benefits
Location: UK Remote – Occasional travel to hub
Contract Type:Permanent

Be the voice we need. Live a life more Virgin.

Our Team

It’s an exciting time for us as we grow our Fraud Analytics team who have a critical role to play, keeping us and our customers safe.  They are our first line of defence function and responsible for day-to-day fraud system analytics, ownership, management and control.  We’re seeking an inspiring and engaging Fraud Model Governance Lead who is insatiably curious and isn’t afraid to challenge the status quo.

What you’ll be doing

  • Subject matter expert for models used across application, payment and transactional fraud prevention systems.
  • Drive model development across Fraud channels and systems through use of model building technical skills and engagement with wider Data Science colleagues across VM where applicable.
  • Generate insight and monitoring to provide oversight of fraud models to mitigate model risks.
  • Agree and develop model performance monitoring indicators and thresholds to enable controls.
  • Be the point of contact for the MRM & IMV within the fraud team to support model management throughout the model lifecycle.
  • Where applicable liaise with vendors / model providers to facilitate effective model risk management. 
  • Provide model specific support to projects and initiatives within Fraud.  
  • Support the MRM & IMV with model identification and model tiering.
  • Where appropriate escalate model breaches to relevant stakeholder groups.

We need you to have

  • Previous experience managing fraud risk models.
  • In depth knowledge of principals of model risk management set out by the PRA in SS1/23.
  • Expert knowledge of advanced analytics techniques, including machine learning, anomaly detection, predictive modelling, and statistical analysis.
  • Strong coding skills including SAS and Python.
  • Excellent analytical and problem-solving abilities, with the capacity to make data-driven decisions.
  • Excellent stakeholder management skills.
  • Strong communication skills, able to translate complex statistical data into real world insight.

It’s a bonus if you have but not essential

  • Knowledge of fraud systems and system optimisation. 
  • Knowledge of how models are used to detect and prevent fraud.

Red Hot Rewards

  • Generous holidays - 38.5 days annual leave (including bank holidays and prorated if part-time)​ plus the option to buy more.
  • Up to five extra paid well-being days per year​. 
  • 20 weeks paid, gender-neutral family leave (52 weeks in total) for expectant parents and those looking to adopt. 
  • Market-leading pension.
  • Free private medical cover, income protection and life assurance.
  • Flexible benefits include Cycle to Work, wellness and health assessments, and critical illness. 

And there's no waiting around, you'll enjoy these benefits from day one.

Feeling insatiably curious about this role? If we’re lucky to receive a lot of interest, we may close the advert early and would hate you to miss out.

We're all about helping youLive a Life More Virgin, so happy to talk flexible working with you.

Say hello to Virgin Money
We’re making great strides towards achieving our ambition of becoming the UK’s best digital bank.  As a full-service digital bank with a heritage stretching back over 180 years, we`re a workforce to be reckoned with.  We're putting the full power of our experience behind disruptive ideas that reinvent the role a bank plays in people's lives. We're customer obsessed and work tirelessly to create positive experiences for our millions of customers and deliver on our purpose, ‘Making You Happier About Money.’ Our customer centric culture means that we're able to do banking differently and by innovating and working together we can make a real difference. Join us and Live a Life More Virgin that empowers you with choice and flexibility in how you work.

Be yourself at Virgin Money
Our purpose is to make people happier about money, this means seeing and feeling the world as our customers do by creating a workforce that reflects the rich diversity of our customers and communities.  We’re committed to creating an inclusive culture where colleagues feel safe and inspired to contribute, speak up and be heard.  

As a Disability Confident Leader, we're committed to removing any obstacles to inclusion.  If you need any reasonable adjustments or support making your application, contact our Talent Acquisition team

Now the legal bit
Living A Life More Virgin allows our colleagues to be based anywhere in the UK (if the role allows it), but we'll need you to confirm you have the right to work in the UK.

If you're successful in securing a role with us, there are some checks you need to complete before starting. These include credit and criminal record checks and three years' worth of satisfactory references. If the role is part of the Senior Manager Regime and Certification Regime, it requires enhanced pre-employment checks – we'll ask for six years of regulatory references, and once in the role, you'll be subject to periodic employment checks. 

 

 

 

 

 

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.