Business Unit: Group Risk, Risk Analytics
Salary - Up to circa £40,000 DOE
Contract Type: Permanent

Your life more Virgin starts here

Virgin Money is all about disrupting the status quo, taking on the challenges of the future, and saying goodbye to boring banking. We’re on the lookout for like-minded individuals and innovators like you to help push us forward. We’re also transforming how we work to give our colleagues more choice, control and flexibility to live and work how they choose with A Life More Virgin.

Here’s what it means for YOU:

• 38.5 days annual leave for all colleagues (including bank holidays and pro rata if part-time)​
• Five extra paid well-being days per year​
• A 35-hour working week
• 20 weeks paid, gender neutral family leave (52 weeks in total) for expectant parents and those looking to adopt
• Market leading Pension & Private Medical Insurance
• Flexible benefits including Cycle to Work, Wellness & Health Assessments and Critical Illness
• Ability to work anywhere in the UK​ (where the role allows)

And you get these Red-Hot Rewards from day one!

Our Risk department provide us with a fool proof framework to make decisions that we know will keep our customers’ and investors’ money safe.  They're always safeguarding our bank from an array of risks that could pop up at any moment.

We draw on Statistical Modelling techniques to produce the best possible predictive models. Working collaboratively with subject matter experts and business specialists, the models we produce quantify risk to inform decision making across the bank on a range of real-world problems. The work we do is key to the bank (and wider economy) and so our models are reviewed by internal experts and regulators.  In addition, we’re experimenting with the use of Machine Learning techniques to inform our model development.

We’re seeking bright individuals who have some experience in modelling and analytics. Your experience can be in a range of contexts such as; Pharmaceuticals, Academia, Banking, Data Science etc. Importantly, you should be goal focused and have an ability to think critically about modelling and statistics.  This is a role where you will work with and, learn from our multidisciplinary team of experts. As well as regular feedback, formal training courses are provided to help you make the most of your career and develop you as an individual. We ask that you bring a desire to develop, research and collaborate, as well a record of producing high quality work.

It’s an exciting time to be joining Virgin Money as we transform our team into one that is ready to meet the challenges of the future, setting us up to support our ambition to disrupt the status quo and turn traditional banking on its head. We’re on the lookout for like-minded individuals and innovators to help drive our strategy forward. We’re also transforming how we work, offering our colleagues more choice, control and flexibility to live and work how they choose with A Life More Virgin.

Day to day you’ll be responsible for…

• Building and validating statistical models to support business decisions, in line with regulatory requirements.
• Researching the best techniques for the given purpose and coordinating with others to determine a model specification.
• Providing a critical eye on existing models and technical expertise to stakeholders.
• Drafting model documentation and recommendation papers.
• Managing own workload and prioritise accordingly.
• Providing input into the scoping, design, development, validation and implementation models.
• Data preparation, merger, quality checks and treatment.
• Providing support and coaching to less experienced analysts within the team.

There are a few essentials you need to bring…

• A demonstrated ability to learn complex mathematical and coding techniques.
• A numerical related degree (Maths, Statistics, Physics, Astronomy, Data Science, Econometrics or related) or relevant business experience.
• Some understanding of statistical modelling techniques such as hypothesis testing, probability distributions, Linear and Logistic Regression.
• Some experience of model development with a track record of producing high quality analytical work.
• Experience in the coding models in Python, R, SAS, MatLab or related 
• A passion for analysing and interpreting data.
• Good communication, listening and writing skills. An ability to listen, understand and respond professionally.

It’s a bonus if you have..but not essential

• Understanding of machine learning model algorithms such as; Boruta, XGBoost and Random Forests.
• A post graduate numerical related degree (Maths, Statistics, Physics, Astronomy, Data Science, Econometrics or related).
• Understanding of model monitoring reports and key metrics of model efficacy (GINI/ROC-AUC, KS statistic etc.)
• Experience in Banking and Risk Management.
• Understanding of model usage and credit strategies.
• Coaching/mentoring experience.
• Knowledge of the Basel Capital Requirements Regulation.

We really need you to have the skills and experience listed in the essentials you’ll need to bring section above, but the rest is just our wish list, so please don’t let that put you off applying, we’d love to hear from you!

Inclusion at Virgin Money

Inclusion is at the heart of everything we do here at Virgin Money. It’s good for you, it’s good for us and it’s amazing for our customers. We know that great minds don’t think alike, so we rely on your diverse thoughts, feelings, beliefs and backgrounds to be the best we can possibly be. Got any questions about this or need some support with your application? We’d love to hear from you so get in touch with our careers team at careers@virginmoney.com.

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 will need to you to confirm you have the Right to Work in the UK.

If we offer you a job and you accept, there are some checks we need to complete before you can start with us. This will include a credit and criminal record check, as well as providing 3 years' worth of satisfactory references.