At a Glance
- Tasks: Design and monitor predictive models to enhance credit decisions and customer outcomes.
- Company: Join the innovative team at Very, dedicated to helping families thrive.
- Benefits: Flexible working, generous holiday, learning opportunities, and discounts on products.
- Other info: Inclusive culture with excellent career growth and support for all applicants.
- Why this job: Make a real impact on lending decisions for millions of customers.
- Qualifications: Recent graduates in quantitative fields; coding experience is a plus.
The predicted salary is between 30000 - 40000 € per year.
About us
We’re the team behind digital retailer Very. Our purpose, helping families get more out of life, powers everything we do. And we want our people to get more out of life too! If you’re high-performing, ambitious and make the most of every opportunity, we want to hear from you. In return, you’ll enjoy heaps of flexibility, great perks and benefits, and the freedom to be yourself, keep learning and take your career wherever you want it to go. If you love making a difference, you’ll love making it sparkle for millions of Very customers.
About the role
Our in-house credit risk and analytics teams are the backbone of our business, leveraging advanced analytical techniques to ensure we lend responsibly and sustainably. As a credit risk modelling analyst, you’ll design, deploy, and monitor cutting‑edge predictive models that empower smarter decisions at every stage of the credit lifecycle. From developing machine learning data‑driven models used in risk assessment to detecting fraudulent transactions in real time, you’ll work on a diverse range of projects that have a real impact on our customers.
About you
Talented and ambitious graduates with a degree in a quantitative field such as Statistics, Mathematics, Data Science or a related discipline (we are open to candidates with commercial experience). Highly intellectual and curious mind with a passion for using data to solve problems. A proactive and self‑motivated attitude with a keen attention to detail. Strong communication skills to convey complex concepts to both technical and non‑technical stakeholders. Experience in coding with SQL, Python and/or developing statistical models (linear/logistic regression, machine learning etc) is advantageous but not a requirement.
Key Responsibilities
- Develop predictive models using advanced statistical methods (e.g., logistic regression, XGBoost, neural networks) to empower decisions across the credit lifecycle.
- Continuously monitor model performance metrics (e.g. Accuracy, Gini, stability), troubleshoot deviations, and report findings to key stakeholders.
- Collaborate cross‑functionally to deploy models and quantify impacts on customer outcomes and commercial value.
- Research new modelling techniques (e.g. machine learning/AI) and alternative data sources to enhance predictive power and drive value.
Why join us?
Real World Impact: Your work will directly influence lending decisions and risk policies for over 3 million customers.
Career Development: Tailored development plans will allow you to develop into a senior analyst with further opportunities to move into a managerial or lead analyst role.
Culture: Collaborative, inclusive, and flexible work environment, check out our Glassdoor reviews.
Some of our benefits
- Flexible, hybrid working model.
- Inclusive culture and environment, check out our Glassdoor reviews.
- £250 flexible benefits allowance to suit your needs.
- 27 days holiday + bank holidays.
- Udemy learning access.
- Bonus potential (performance and business‑related).
- Up to 25% discount on Very.co.uk.
- Matched pension up to 6%.
- More benefits can be found on our career site.
Diversity, inclusion and equal opportunities
We’re building a culture of everyday inclusion, and welcome applications from anyone who believes they can do the job. We don’t discriminate based on age, disability, gender reassignment, marriage or civil partnership, pregnancy or maternity, race, religion or belief, sex, or sexual orientation. We want our recruitment process to be accessible to everyone. If you need reasonable adjustments to apply, interview, or perform a role, let us know via talentacquisition@theverygroup.com. We’ll be happy to support you. We’re proud to be a Disability Confident Committed Employer and have nine brilliant colleague networks - including DAWN (Disability Awareness at Very) and Think (Neurodiversity at Very) – that are helping us make Very an even more inclusive place to work.
Credit Risk Modelling Analyst employer: Very Group
At Very, we are dedicated to empowering our employees just as much as we empower our customers. With a flexible hybrid working model, a collaborative and inclusive culture, and tailored career development plans, we ensure that every team member can thrive and grow in their role. Join us to make a real-world impact while enjoying generous benefits like a £250 flexible allowance, 27 days of holiday, and access to continuous learning opportunities.
StudySmarter Expert Advice🤫
We think this is how you could land Credit Risk Modelling Analyst
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend events, and connect on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Prepare for interviews by practising common questions and showcasing your analytical skills. Think about how you can demonstrate your problem-solving abilities with real-world examples from your studies or projects.
✨Tip Number 3
Don’t just apply anywhere; focus on companies that align with your values and career goals. We at StudySmarter recommend checking out our website for tailored opportunities that match your skills and aspirations.
✨Tip Number 4
Follow up after interviews! A quick thank-you email can leave a lasting impression and show your enthusiasm for the role. It’s a simple way to stand out from the crowd.
We think you need these skills to ace Credit Risk Modelling Analyst
Some tips for your application 🫡
Show Your Passion for Data:When you're writing your application, let us see your enthusiasm for using data to solve problems. Share any projects or experiences that highlight your analytical skills and curiosity. We love candidates who are eager to make a difference!
Tailor Your CV and Cover Letter:Make sure your CV and cover letter are tailored to the role of Credit Risk Modelling Analyst. Highlight relevant coursework, projects, or experiences that align with our needs. This shows us you’ve done your homework and are genuinely interested in joining our team.
Be Clear and Concise:We appreciate clarity! When describing your experiences and skills, be straightforward and avoid jargon. Use simple language to convey complex concepts, as this will demonstrate your strong communication skills, which are key for this role.
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it gives you a chance to explore more about our culture and benefits while you’re at it!
How to prepare for a job interview at Very Group
✨Know Your Numbers
As a Credit Risk Modelling Analyst, you'll be dealing with data and statistics. Brush up on your knowledge of predictive modelling techniques like logistic regression and machine learning. Be ready to discuss how you've used these methods in your studies or any projects.
✨Show Your Curiosity
Demonstrate your passion for data by discussing recent trends in credit risk analytics or new modelling techniques you've researched. This shows that you're not just knowledgeable but also eager to learn and grow in the field.
✨Communicate Clearly
You’ll need to explain complex concepts to both technical and non-technical stakeholders. Practice articulating your thoughts clearly and concisely. Use examples from your academic work or internships to illustrate your points.
✨Prepare for Real-World Scenarios
Think about how you would approach real-world problems, such as detecting fraudulent transactions. Prepare to discuss hypothetical scenarios during the interview, showcasing your analytical thinking and problem-solving skills.
