At a Glance
- Tasks: Drive data-driven lending decisions and improve credit risk models in a fast-paced environment.
- Company: GOCAP, a forward-thinking lender focused on affordability and sustainability.
- Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
- Other info: Join a small, collaborative team with high visibility and ownership from day one.
- Why this job: Make a real impact on lending strategies and customer outcomes with your analytical skills.
- Qualifications: 5+ years in credit risk modelling, advanced Python and SQL skills required.
The predicted salary is between 60000 - 80000 £ per year.
London | Full-time | Hybrid (2 days in office)
About GOCAP
GOCAP is building a different kind of lender. We provide salary-linked personal loans designed around real affordability and repayment sustainability. We are live in market, growing quickly, and using data to improve how lending decisions are made.
This is a hands-on role within a small team where your work will directly influence underwriting, pricing, portfolio performance and customer outcomes. We are looking for someone analytical and pragmatic. The role is more than just building good models. We value practical, explainable and operationally effective credit risk solutions that drive strategic value to the business. You will have key input in this process.
The role
You will work closely with the VP of Credit across credit lifecycle, helping to develop and improve the analytical approaches that drive our lending decisions. This includes building models, analysing performance, and identifying opportunities to improve how we assess and manage risk in practice.
What you'll do
- Work within AWS-based production environments, including testing, maintaining and supporting changes across credit decisioning workflows and Lambda-driven processes
- Analyse originations, arrears, defaults, cures and portfolio trends
- Build and improve credit risk and decisioning models with bureau, open banking and internal lending data
- Analyse and maintain structured loan and portfolio data tapes
- Produce clear, decision-ready insights from imperfect or incomplete datasets
- Support implementation and refinement of underwriting strategies
- Build monitoring and reporting to track model and portfolio performance
- Work closely with risk, product and engineering to operationalise changes
- Investigate data quality, segmentation and performance issues
- Support model governance, validation and documentation
What we're looking for
- 5+ years building, calibrating and productionising credit risk models in live decisioning environments, with demonstrated experience beyond traditional scorecard methodologies (e.g. machine learning, hybrid/ensemble or data-driven segmentation approaches)
- 3+ years' experience working within AWS-based decisioning environments, including testing and supporting changes across production workflows and services such as Lambda.
- 2+ years' experience working in lending or live credit decisioning environments
- Advanced Python and SQL skills are mandatory
- Experience working with real-world and imperfect datasets
- Understanding of underwriting, scorecards, portfolio analytics or risk strategy
- Strong analytical and problem-solving skills
- Ability to explain analysis clearly and commercially
- Comfortable working in a fast-moving environment with high ownership
Particularly valuable
- Experience in fintech or bureau functions
- Hands on experience in analysing bureau and transactional data e.g. open banking
- Experience implementing or monitoring production credit models
- Understanding of lending trade-offs beyond pure model performance metrics
- Familiarity with model monitoring, segmentation and performance stability
- Ability to challenge assumptions and defend analytical reasoning constructively
What to expect
- A small, fast-moving and highly collaborative environment
- Direct exposure to live lending decisions and portfolio performance
- High visibility and ownership from day one
- Opportunity to help shape how a growing lender uses data in decisioning
Apply
Please send your CV along with a short note outlining:
- the type of analytical work or models you have implemented
- your experience in working within AWS-based environments
- your relevant lending or credit risk experience
- and why the role interests you
Please note: You must have the right to work in the UK. We are unable to provide visa sponsorship.
Senior Data Scientist | Credit Risk in London employer: GOCAP
At GOCAP, we pride ourselves on being an innovative lender that values data-driven decision-making and employee empowerment. Our hybrid work model fosters a collaborative environment where your contributions directly impact our lending strategies and customer outcomes. With ample opportunities for professional growth and a commitment to operational excellence, GOCAP is an excellent employer for those looking to make a meaningful difference in the credit risk landscape.
StudySmarter Expert Advice🤫
We think this is how you could land Senior 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 that a CV just can't.
✨Tip Number 2
Prepare for the interview by brushing up on your AWS skills and credit risk knowledge. Be ready to discuss how you've tackled real-world data challenges in the past.
✨Tip Number 3
Showcase your analytical prowess! Bring examples of your work with credit risk models and be prepared to explain your thought process clearly and commercially.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets noticed and shows you're serious about joining the team.
We think you need these skills to ace Senior Data Scientist | Credit Risk in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Senior Data Scientist role. Highlight your experience with credit risk models and AWS environments, as these are key for us. Use specific examples that showcase your analytical skills and how you've made an impact in previous roles.
Craft a Compelling Note:When writing your short note, be clear and concise. Focus on the analytical work you've done, especially in lending or credit risk. Let us know why this role excites you and how you can contribute to our mission at GOCAP.
Showcase Your Skills:Don’t forget to highlight your advanced Python and SQL skills! We want to see how you've used these in real-world scenarios, especially in production environments. This will help us understand your technical capabilities better.
Apply Through Our Website:We encourage you to apply through our website for a smoother process. It’s the best way for us to receive your application and ensures you don’t miss any important details about the role!
How to prepare for a job interview at GOCAP
✨Know Your Data Inside Out
Make sure you’re well-versed in the datasets you’ll be working with. Brush up on your experience with real-world, imperfect datasets and be ready to discuss how you've tackled data quality issues in the past. This will show that you can handle the practical challenges of the role.
✨Showcase Your AWS Expertise
Since the role involves working within AWS-based environments, be prepared to talk about your hands-on experience with AWS services like Lambda. Share specific examples of how you've tested and supported changes in production workflows, as this will demonstrate your technical proficiency.
✨Explain Your Analytical Approach
Be ready to discuss your analytical methodologies, especially beyond traditional scorecard techniques. Highlight any machine learning or hybrid approaches you've used in credit risk modelling, and explain how these have driven strategic value in your previous roles.
✨Prepare for Practical Scenarios
Expect to face scenario-based questions that assess your problem-solving skills. Think about how you would approach real lending decisions and portfolio performance issues. Practising these scenarios will help you articulate your thought process clearly during the interview.
