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For Companies At a Glance
- Tasks: Build and improve data models for credit risk and pricing while analysing customer behaviour.
- Company: GOCAP, a pioneering lender reshaping the market with innovative salary-linked loans.
- Benefits: Competitive salary, growth opportunities, and a chance to make a real impact.
- Other info: Exciting growth phase with opportunities for personal and professional development.
- Why this job: Join a dynamic team at the heart of data-driven decision-making in finance.
- Qualifications: Experience in fintech or credit risk, advanced Python and SQL skills required.
The predicted salary is between 50000 - 70000 £ per year.
GOCAP is building a better kind of lender. We have created a salary-linked consumer loan designed to align repayments more closely with real affordability. It is a differentiated product, a genuinely interesting credit proposition, and we believe it has the potential to reshape part of the market. We are now entering an exciting growth phase. The business is live, performing strongly, and moving towards significant scale. As we grow, data science will sit at the heart of how we underwrite, price, monitor risk and improve performance.
We are looking for a sharp, hands-on Data Scientist to help drive that forward. You will be expected to deliver quickly, iterate based on evidence, and continuously improve decisioning as new data becomes available.
What you'll do
- Build and improve models across credit risk, pricing and portfolio performance
- Analyse originations, arrears, defaults, cures and customer behaviour
- Support underwriting strategy with data-driven insights and segmentation
- Work with bureau, open banking and manage internal data pipelines
- Produce clear, decision-ready insights from imperfect data
- Support and refine credit strategy through analysis and segmentation
- Build monitoring and reporting that tracks real performance
- Help strengthen model governance, validation and documentation
- Work closely with risk, product and engineering to turn analysis into action
What we're looking for
- Experience in lending, fintech, banking or credit risk
- Advanced Python and SQL skills
- Strong analytical and statistical modelling skills
- High ownership and attention to detail
- Experience with underwriting, scorecards, pricing or portfolio analytics
- Experience working with real-world datasets
- Commercial judgement and clear communication
Preferred
- Familiarity with credit decisioning using bureau and open banking data
- Experience in building Machine Learning models which convert into decision-ready outputs
- Ability to work with unstructured and incomplete datasets
- Self-motivated and solutions oriented, willing to challenge senior stakeholders
- Comfortable having your work challenged and defending your logic
- Bachelor’s or Master’s degree in Data Science, Statistics or a related field
We are not looking to outsource this role to external recruiters at this stage.
Data Scientist - Credit Risk in London employer: GOCAP
Contact Detail:
GOCAP Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist - Credit Risk in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those at GOCAP. A friendly chat can open doors and give you insights that a job description just can't.
✨Tip Number 2
Show off your skills! Prepare a portfolio of your data science projects, especially those related to credit risk or lending. This will help you stand out and demonstrate your hands-on experience.
✨Tip Number 3
Be ready for a challenge! During interviews, expect to discuss your thought process and defend your decisions. Practise explaining your work clearly and confidently – it’s all about showcasing your analytical skills.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in being part of the GOCAP team.
We think you need these skills to ace Data Scientist - Credit Risk in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the role of Data Scientist in Credit Risk. Highlight your experience in lending, fintech, and any relevant projects that showcase your analytical skills. We want to see how you can contribute to our exciting growth phase!
Show Off Your Skills: Don’t hold back on showcasing your advanced Python and SQL skills! Include specific examples of how you've used these tools to build models or analyse data. This is your chance to impress us with your technical prowess.
Craft a Compelling Cover Letter: Your cover letter should tell us why you're passionate about credit risk and how you can help reshape the market with your insights. Be genuine and let your personality shine through – we love to see enthusiasm for what we do!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity. Plus, it shows us you’re keen to be part of our journey!
How to prepare for a job interview at GOCAP
✨Know Your Data Science Stuff
Make sure you brush up on your Python and SQL skills before the interview. Be ready to discuss your experience with credit risk models and how you've used data to drive decisions. They’ll want to see that you can not only build models but also explain your thought process clearly.
✨Showcase Your Analytical Skills
Prepare to talk about specific projects where you've analysed customer behaviour or improved portfolio performance. Use real-world examples to demonstrate your analytical prowess and how it led to actionable insights. This will show them you can turn data into decisions.
✨Understand the Business Context
Familiarise yourself with GOCAP’s unique approach to lending and their product offerings. Being able to connect your data science skills to their business model will impress them. Show that you understand how your role as a Data Scientist fits into their growth strategy.
✨Be Ready for Technical Questions
Expect some technical questions about model governance, validation, and working with unstructured datasets. Prepare to defend your logic and thought processes. They want to see that you can handle challenges and think critically about your work.