At a Glance
- Tasks: Analyse data to support small businesses and enhance credit risk strategies.
- Company: Funding Circle, a fintech leader dedicated to empowering UK small businesses.
- Benefits: Competitive salary, flexible work, health perks, and personal development opportunities.
- Other info: Dynamic team culture with a focus on collaboration and growth.
- Why this job: Join a mission that makes a real impact on the UK economy.
- Qualifications: Degree in a data-related field and strong analytical skills.
The predicted salary is between 35000 - 45000 £ per year.
We’re on a mission to back the UK’s small businesses like no one else. Small businesses are the backbone of the economy, and we’re here to help them win. We’ve built a platform that uses clever data to get them the funding they need in minutes, not weeks.
At Funding Circle, we have the restless energy of a fintech start-up with the stability of a public company. It’s a unique mix that gives Circlers the autonomy to take ownership and the scale to make an impact that truly counts. We’re a high-performing team that chooses to lift each other up. We challenge, we champion, and we have each other’s backs because we know that when we stand together, we move faster and build better.
The impact is real: Last year alone, the businesses on our platform generated £7.2bn for the UK economy. Come and join a mission that matters.
The role:
- Location: London (Hybrid) | 3 days in the office | Competitive Salary + Benefits
This role sits within the Second Line Credit Strategy team. We provide independent oversight of credit risk models, strategies, and the data assets that underpin them. You will own a critical piece of our data infrastructure and play a hands-on role in making sure the team can do its best analytical work.
The primary responsibilities include:
- Maintaining field logic, monitoring data quality, and ensuring the data as well as the dictionary remains accurate and fit for purpose.
- Creating new tables and pipelines as the team’s analytical needs evolve, working to a high standard of code quality and documentation.
- Proactively identifying anomalies, breaks, and drift in the datamart, and investigating root causes before they affect downstream users.
- Being the go-to person for data questions from analysts and model validators, and maintaining clear documentation so others can self-serve confidently.
- Working closely with the central Data team to align on tooling, infrastructure, and best practice, and promoting those to the Credit Risk Team.
What we’re looking for:
- A degree from a numerical or data-related subject, such as Mathematics, Computer Science, Engineering, Physics, or Data Science.
- 1–2 years of work experience, or a recent graduate with demonstrably strong data skills.
- Proficient in SQL, familiar with Github, and confident using Tableau to build and maintain dashboards.
- Comfortable working independently on ambiguous, open-ended data problems with a methodical and detail-oriented approach.
- A clear communicator, able to document logic and flag data issues in plain language for both technical and non-technical colleagues.
- Familiarity with Python, dbt, Airflow, or AWS is a plus, as is MS Office in a professional setting.
- Able to take ownership of redesigning departmental workflows, using AI to automate complex tasks and drive operational efficiency.
- Competently able to identify high-impact AI use cases and implement advanced prompting techniques to solve multi-layered business challenges.
We’re building a place where everyone truly feels they belong. Even if your past experience doesn't align perfectly with every requirement, we'd still love to hear from you.
Why join us?
- We back you to build an incredible career and by joining us, you'll be part of one of The Sunday Times Best Companies to Work For 2026!
- As a flexible-first employer, we use a "best of both" approach. We’ll see you in our London office to collaborate – with barista coffee and subsidised Just Eat lunches on us!
Our Circler Proposition focuses on five areas:
- Flexibility: We provide a benefit allowance you can tailor to your own life and family.
- Health: This includes private medical and dental, health assessments, and access to a digital GP.
- Wealth: We offer life assurance, share schemes, and financial coaching.
- Development: You get a dedicated annual learning allowance to help you level up.
- Lifestyle: We have electric car and cycle-to-work schemes, plus season ticket loans.
We also have award-winning parental leave policies. We're here to support you through the big life moments, from fertility treatments to new additions to the family.
Ready to join a mission that matters? We’d love to chat!
Data Analyst in London employer: Funding Circle
At Funding Circle, we are dedicated to empowering the UK’s small businesses, and as a Data Analyst in our London office, you will be part of a dynamic team that values collaboration and innovation. With a flexible work culture, competitive benefits, and a strong focus on personal development, we provide an environment where you can thrive and make a meaningful impact. Join us and enjoy perks like tailored benefit allowances, health support, and a commitment to your career growth in a company recognised as one of The Sunday Times Best Companies to Work For 2026.
StudySmarter Expert Advice🤫
We think this is how you could land Data Analyst in London
✨Get Involved in Data Science Meetups
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We think you need these skills to ace Data Analyst in London
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 Funding Circle, 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 Funding Circle. 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 Funding Circle
✨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!
✨Showcase Your Projects
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 Funding Circle!
✨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.
