At a Glance
- Tasks: Analyse data to shape credit risk strategies and drive business decisions.
- Company: Join a top UK fintech recognised for innovation and community spirit.
- Benefits: Real Living Wage employer, hybrid working, and opportunities for professional growth.
- Other info: Dynamic team culture celebrating diversity and collaboration across global locations.
- Why this job: Make a real impact by turning data into actionable insights for small businesses.
- Qualifications: Strong analytical skills, experience with SQL or BigQuery, and a passion for problem-solving.
The predicted salary is between 34650 - 42350 £ per year.
Some key info for you about Liberis:
- Founded in 2007
- Provided over $3bn of funding to small businesses so far
- Named in CNBC & Statista Top 150 UK Fintechs for 2025
- A global team with a dynamic presence in 6 key locations around the world
- A thriving community of over 290 innovative minds
- A vibrant melting pot, celebrating over 27 nationalities in our team
- Experience from over 740 previous companies, from startups to global giants
- Named as one of FinTech’s Finest 50 by Welcome to the Jungle
- Proud to be an accredited Real Living Wage employer
The team is responsible for managing the credit risk of Liberis’ portfolios, which include designing effective new strategies, managing the credit quality of our products across geographies, and providing specialist credit risk advice to the business.
The Risk team is based in London and covers Risk analytics, Decision Science, Underwriting and Collections.
We are looking for a Credit Risk Analyst to join us on this journey. We're looking for someone who enjoys solving complex problems through data and analysis. This is an ideal role for an analytical thinker who is naturally curious, enjoys investigating trends and identifying opportunities, and is motivated by seeing their work influence real business decisions.
You will be comfortable taking ownership of projects - framing problems, conducting deep analysis, developing recommendations and working with stakeholders to implement change. While experience in credit risk is beneficial, we're primarily looking for an individual with strong analytical capability, commercial thinking and a track record of turning data into actionable insights.
Reporting to the Risk Manager, you'll help shape Liberis' credit risk strategy by delivering analytical insights, developing portfolio monitoring frameworks and driving strategic initiatives across our global products. You'll work closely with teams across Risk, Data Science, Analytics, Product, Pricing and Commercial to ensure our lending decisions support sustainable growth while remaining within our risk appetite.
What you'll get to do:
- Build and improve portfolio monitoring frameworks, turning data into clear insights and recommendations.
- Monitor losses, delinquencies, inactivity, repayment trends and vintage performance to assess portfolio health and identify emerging risks.
- Investigate changes in performance, explain their causes and identify opportunities to improve outcomes or support growth.
- Use your analysis to inform credit risk strategies, including where customer eligibility could be expanded or tightened.
- Develop forecasting models to better understand future risk and contribute to budgeting and planning.
- Work with colleagues across risk and the first line to understand problems, communicate findings and recommend action.
- Deliver analytical projects across the portfolio, showing how your work contributes to business decisions rather than stopping at a report or dashboard.
The interview process includes:
- Screening call with Chess (Internal Recruiter) (30 mins)
- Video interview with the Hiring Manager (1 hour)
- Task based interview with the Risk Team (90 mins)
What we think you'll need:
- Hands-on data analysis skills, including SQL or BigQuery experience, and the ability to apply those skills to unfamiliar tools.
- The ability to move from data to an explanation: identifying patterns, investigating why they're happening and making practical recommendations.
- Clear communication, including the ability to explain your own contribution to a project, your analytical approach and what changed as a result.
If this opportunity feels like the right fit for your next career move, we’d love to hear from you! Even if you don’t meet every requirement, don’t hesitate to apply or reach out to Chess (Internal Recruiter).
Our hybrid working approach requires colleagues to attend the office at least three days a week, increasing to at least four days a week in Q1 2027.
Data Analyst - Credit Risk in Vauxhall employer: Liberis
At Liberis, we pride ourselves on being an excellent employer, offering a vibrant work culture that celebrates diversity and innovation. With a commitment to employee growth, our hybrid working model fosters collaboration while providing flexibility, ensuring that our team members thrive both personally and professionally. As a Real Living Wage employer, we value the contributions of our staff and provide meaningful opportunities to shape the future of embedded finance in a supportive environment.
StudySmarter Expert Advice🤫
We think this is how you could land Data Analyst - Credit Risk in Vauxhall
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We think you need these skills to ace Data Analyst - Credit Risk in Vauxhall
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Liberis, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Liberis. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Liberis
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Liberis!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
