Role Overview
We're looking for a Data Scientist to join the iwocaPay Risk Squad. You'll own credit models that decide who iwocaPay can safely fund and on what terms. The team is small, the product is growing, and you'll shape how risk modelling is built – not just maintain what exists.
The Company
Small businesses move fast. Opportunities often don’t wait, and cash flow pressures can appear overnight. To keep going, and growing, SMEs need finance that’s as flexible and responsive as they are. That's why we built iwoca. Our smart technology, data science and five-star customer service ensures business owners can act with the speed, confidence and control they need, exactly when it's needed. Our ultimate mission is to support one million SMEs in their defining moments, creating lasting impact for the communities and economies they drive.
The Function and Team
iwoca's Data Scientists specialise in supervised machine learning, statistical inference and exploratory data analysis, focusing on tabular and time series data. iwocaPay is iwoca's B2B Buy Now Pay Later product. The Risk Squad owns the automated credit decisioning and fraud prevention models that influence every lending decision the product makes. The squad also handles counterparty risk, portfolio-level strategy, and ongoing monitoring of losses, chargebacks, and segment performance.
The Role
You'll co-own the full credit modelling stack with one other data scientist. You’ll collaborate on model design, review each other's work, and choose what to prioritise. Your models directly control which small businesses get access to funding, on what terms, and how far the product can grow.
Culture
At iwoca, the best idea wins. We model our culture on independent thinking, challenging untested logic, and evidence-based decisions. We prioritise learning and growth, and give people the autonomy to develop in the direction that makes them most effective. We're a tech company and believe in the power of AI to help us work faster and better.
What you'll get out of it:
- Complex problems to solve: Work on interesting, open modelling problems rather than maintenance.
- Real influence: You own the models end to end, and your decisions carry commercial weight.
- Room to develop: A small team on a growing product means broad remit, fast growth, and a say in how the team's pipelines, monitoring, and processes get built.
Salary
We expect to pay from £60,000 – £80,000 for this role. We routinely benchmark salaries against market rates, and run quarterly performance and salary reviews.
Offices
- Offices in London, Leeds, Berlin, and Frankfurt with plenty of drinks and snacks.
- Events and community-led groups, including running groups, padel, and monthly ping-pong and pool competitions.
Benefits
- Flexible working hours.
- Medical insurance from Vitality, including discounted gym membership.
- A private GP service for you, your partner, and your dependents.
- 25 days’ holiday per year, an extra day off for your birthday, the option buy up to ten more days or up to five, and unlimited unpaid leave.
- A one-month, fully paid sabbatical after four years.
- Instant access to external counselling and therapy sessions.
- 3% Pension contributions on total earnings.
- An employee equity incentive scheme.
- Generous parental leave and a nursery tax benefit scheme.
- Electric car scheme and cycle to work scheme.
- Two company retreats a year.
- A learning and development budget for everyone.
- Company-wide talks with internal and external speakers.
- Access to learning platforms like Treehouse.
Essential:
- Expertise in supervised machine learning on tabular data, with experience building and iterating production models end to end (exploration through deployment and monitoring).
- Strong statistical foundations in probability, uncertainty quantification, model calibration, and in reasoning about where a model is likely to be wrong.
- Ability to work independently in a messy, early-stage environment – scoping problems, building infrastructure, and shipping models autonomously.
- Pragmatism about what ‘good enough’ looks like commercially, balanced with rigour on high-stakes models.
- Data engineering capability – building pipelines and managing datasets alongside the modelling work.
- AI fluency – using AI to build, automate, and accelerate substantive analytical and engineering work.
- Clear communication – you can adapt technical detail for non-technical colleagues working in product, risk, and leadership.
Bonus:
- Experience with credit risk modelling or financial services lending.
- Familiarity with transfer learning, domain adaptation, or techniques for combining datasets of different sizes and relevance.
- Experience with non-linear models (gradient boosting, neural networks on tabular data) in production settings.
- Python proficiency – it's what the team uses day to day.
