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- Tasks: Build and improve models for credit risk and pricing while analysing customer behaviour.
- Company: GOCAP, a pioneering lender reshaping the market with innovative loan products.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Exciting growth phase with opportunities to influence decision-making and model governance.
- Why this job: Join a dynamic team at the forefront of data science in lending and make a real impact.
- Qualifications: Experience in fintech or banking, strong Python and SQL skills, and analytical mindset.
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
Data Scientist - Credit Risk employer: GOCAP
Contact Detail:
GOCAP Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist - Credit Risk
✨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 projects or case studies that highlight your experience with credit risk and data science. This will help you stand out during interviews.
✨Tip Number 3
Be ready to discuss real-world applications of your work. GOCAP is all about practical solutions, so come armed with examples of how you've used data to drive decisions in previous roles.
✨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
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, or banking, and showcase your advanced Python and SQL skills. We want to see how your background aligns with what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're excited about GOCAP and how you can contribute to our growth phase. Be sure to mention your analytical skills and any relevant experience with credit risk or portfolio analytics.
Showcase Your Projects: If you've worked on any projects related to data science, especially in credit risk or machine learning, make sure to include them. We love seeing real-world applications of your skills, so don’t hold back on the details!
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of your application and ensures you’re considered for the role. Plus, it’s super easy to do!
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 insights. They’ll want to see that you can not only build models but also explain your thought process clearly.
✨Show Off Your Analytical Skills
Prepare to talk about specific projects where you've analysed customer behaviour or worked with real-world datasets. Bring examples of how your analysis led to actionable insights, especially in lending or fintech. This will demonstrate your ability to turn data into decisions.
✨Understand Their Business Model
Familiarise yourself with GOCAP's salary-linked consumer loan product. Think about how data science can enhance their underwriting strategy and improve performance. Showing that you understand their unique approach will set you apart from other candidates.
✨Be Ready for Technical Questions
Expect questions that challenge your logic and decision-making process. They might ask you to defend your modelling choices or how you would handle incomplete datasets. Stay calm, think critically, and don’t hesitate to explain your reasoning clearly.