The team
Our Data Science team helps Zopa make better credit decisions. We partner closely with Credit Strategy, Product and Engineering to turn ambiguous business problems into robust models, clear analysis and practical solutions.
We are growing a dedicated, product-facing data science capability focused predominantly on credit. The team will help rebuild and expand the models that support consumer-credit decisions, while increasing the sophistication of how we model risk, value and outcomes.
This is a hands-on individual-contributor role in a lean, collaborative environment. You will have real ownership, with space to shape the work and build alignment across the people needed to make it happen.
We are hiring at both mid and senior level, and will assess candidates at the level that best reflects their experience, technical depth and scope of impact.
A day in the life
- Take ambiguous credit-related questions from stakeholder discussion through to practical modelling and analysis
- Build, improve and maintain models that support consumer-credit decisions
- Work on flagship risk models and broader value-driver models, including revenue, profit prediction and customer lifetime value
- Use Python and sound statistical judgement to develop classification and regression solutions
- Partner with Credit Strategy to understand priorities and create useful, well-framed solutions
- Collaborate with Product and Engineering to sequence work and support productionisation
- Explain technical choices clearly, build consensus where views differ and help move decisions forward
- Own your problems and delivery, while contributing to a low-ego team that works together
About you
- You have hands-on data science experience
- Bring practical Python and Git capability
- Understand common statistical-learning models and machine-learning algorithms for classification and regression
- Have sound statistical fundamentals, including hypothesis testing and experimental design
- Can independently take an ambiguous problem from discussion to a useful model or analysis
- Communicate clearly and confidently with technical and non-technical stakeholders
- Build alignment when there are differing views, and enjoy working collaboratively to move things forward
- Are curious, practical and comfortable operating with limited hand-holding
- Work effectively across business, Product and Engineering partners
- Experience in consumer credit, lending, credit cards or a closely related credit-risk domain
- Exposure to sequence-based deep-learning or transformer-style models
- Experience building production-grade Python microservices
Added bonus
At Zopa we value flexible ways of working.
We value face-to-face collaboration and a good work-life balance. This hybrid role requires you to come to our London office 2-3 days a week.
You'll also have the option of working from abroad for up to 120 days a year!* But no matter where you are, we’ll make sure you’ve got everything you need to thrive, both in your work and home life, from day one.
*Subject to having the right to work in the country of choice
Diversity Statement
Zopa is proud to offer a workplace free from discrimination. Diversity of experience, perspectives, and backgrounds leads to better products for our customers and a unique company culture for our people. We are made up of nearly 50 nationalities, have a DE&I forum made up of Zopians wanting to make a difference and we are proud of our culture where everyone can bring their full self to work. Our approach to DE&I is reflected in our hiring process so please let us know if you require any reasonable adjustments.
Our approach to AI in interviews
At Zopa, AI isn't something we're testing out — it's part of how we work every day. As a proud partner of Jobs 2030, we're committed to building AI fluency across our workforce, and we expect Zopians to use AI as part of how they do their jobs.
Because of that, we want to be transparent about how we think about AI use during our hiring process.
Behavioural and competency-based interviews: please don't use AI. These conversations are designed to understand you — your experiences, your judgment, and how you've approached real situations. An AI-generated answer can't tell us that. What it can do is get in the way of us finding out whether we're the right fit for each other.
Technical interviews: it depends on the role. Some technical stages actively welcome AI use, others don't. Your Talent Partner will let you know what's expected at each stage. Where AI is part of the assessment, we'll be interested not just in the outcome, but in how you used it – the tools you chose, your reasoning, and the decisions you made along the way.
Zopa Bank London, England Office
Tooley Street, London, United Kingdom, SE1 2QG
