At a Glance
- Tasks: Develop and optimise machine learning models for commercial lending products.
- Company: Join a dynamic fintech start-up with the stability of a public company.
- Benefits: Competitive salary, flexible work, barista coffee, and subsidised lunches.
- Other info: Be part of a supportive team that values growth and collaboration.
- Why this job: Make a real impact by helping small businesses thrive with innovative data solutions.
- Qualifications: Degree in a quantitative STEM field and experience in statistical modelling.
The predicted salary is between 63000 - 77000 £ per year.
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!
- (Read our Impact Report
- | (See our Trustpilot
- London (Hybrid) | 3 days in the office | Competitive Salary + Benefits
We are looking for a Data Scientist to join us as we continue to evolve and build our next generation of models at Funding Circle.
You will play a key role in developing, implementing and monitoring these models and drive significant value for the business.
- Model Development: Develop statistical and machine learning models for commercial lending products.
- Optimization: Deliver value through the optimization of current models and drive innovation by constantly seeking alternative data sources.
- Communication: Communicate and present effectively, turning complex analysis into clearly articulated insights.
- Problem Solving: Proactively problem-solve, identifying and mitigating any risks, issues, or control weaknesses in your day-to-day work.
- Experimentation: Actively experiment with various frameworks for model optimization
We value deep expertise, but a growth mindset and good energy are what really make our team click. We're a group that chooses to lift each other up and think smart every day.
As part of the Decision Science team, you will create cutting edge statistical and machine learning models and collaborate with data engineers, analysts, and business stakeholders.
As well as improving on our previous generation models, we are expanding our borrower products so you will have the opportunity to develop completely new models.
- Degree educated in a quantitative STEM discipline with a demonstrated capability to transition theoretical concepts into functional, real-world solutions
- Proven track record of leveraging rigorous quantitative frameworks, structured problem-solving, and analytical methodologies to solve complex business problems.
- End-to-end hands-on experience in statistical modeling, Machine Learning architecture, and deployment, demonstrated through production-level enterprise experience, high-impact research, or advanced project delivery.
- Strong proficiency in Python for data manipulation, statistical analysis, and modeling, with a focus on writing clean, efficient, and reproducible code.
- Deep operational familiarity with SQL, including writing optimised queries, joining complex multi-table datasets, and transforming raw enterprise data into analysis-ready formats.
- Track record of collaborating with cross-functional partners to translate technical outputs into actionable commercial insights, key metrics, or product improvements.
- Able to take ownership and designing workflows, using AI to automate complex tasks and drive operational efficiency
- Identify high-impact AI use cases and implement advanced prompting techniques to solve multi-layered business challenges
Skills we'd love to see
- Prior experience working in Credit Risk or Data Science space
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.
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
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Data Scientist in London employer: Praxy
At Sony Music, we pride ourselves on being an exceptional employer that fosters a vibrant and inclusive work culture in the heart of London. As a Global Latin Music Marketing Intern, you will not only gain invaluable experience in the music industry but also benefit from our commitment to employee growth through mentorship and networking opportunities. Join us to be part of a dynamic team that celebrates creativity and passion for music while enjoying the unique advantages of working in one of the world's most exciting cities.
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist in London
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Praxy!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Data Scientist at Praxy.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Praxy.
✨Apply Directly through Our Website
When you find a suitable opening like Data Scientist at Praxy, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Data Scientist 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 Praxy, 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 Praxy. 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 Praxy
✨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 Praxy!
✨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.
