Data Scientist - Credit Cards

iwoca · United Kingdom · £80k – £100k · posted today ago

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About this role

Data Scientist - Credit Cards

Hybrid in London, United Kingdom

We're looking for a Data Scientist to join the Credit Cards team.

You'll build the credit risk, pricing, and customer lifetime value models behind iwoca's business credit card – who gets a card, with what features, and at what rate.

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.

We've already cleared the way for 100,000 businesses with more than £4 billion in funding. Our passionate team is driven to help even more SMEs succeed, through access to better finance and other services that make running a business easier. Our ultimate mission is to support one million SMEs in their defining moments, creating lasting impact for the communities and economies they drive.

The team

iwoca launched its business credit card at the start of 2026. It gives small businesses a fast, flexible way to pay for what they need and control their cash flow, alongside iwoca's loans. The card is a core part of iwoca's long-term growth.

The Credit Cards team builds the card on iwoca's existing lending infrastructure and customer base. The team is made up of commercial strategy product, engineering, operations, and a data scientists.

The role

As a Data Scientist in Credit Cards, you'll do the analysis and modelling for the card's commercial decisions. You'll often have small samples and incomplete data, which suits Bayesian methods. You'll quantify the uncertainty in each estimate and explain what it means for the decision.

You'll run your own projects, from framing the question with the commercial lead to helping the team act on the result. Your models will shape how the card grows. You will be embedded in a broader Credit Cards Team and learn about the product and customer needs. You'll share methods with Credit Risk Modelling, especially where card and lending models overlap, and with data scientists across iwoca in the analytics guild.

Live project examples:

Credit limits. Setting initial limits that balance risk with what each customer needs, and designing a framework that adjusts them as spending and repayment data comes in.
Pricing. Modelling how interest and fees affect take-up and spending,, to find pricing that works for iwoca and its customers.
Customer lifetime value. Estimating activation, spending, and retention from early signals, and updating the estimates as each cohort matures.
Card and loan together. Measuring cross-sell and combined credit exposure for businesses that hold both the card and an iwoca loan. Designing the mechanisms to optimise how the two products interact.

The requirements

Essential:

Statistical foundations. You have a background in probability and statistics from a quantitative field. You reason about uncertainty and calibration as first-order concerns.
Bayesian reasoning. You've used priors, updating, and hierarchical models in real work. You know when a Bayesian model is worth the extra complexity, and when something simpler will do.
Analytical ownership. You've taken loosely defined problems from framing the question to a decision the business acted on.
Business curiosity. You've used what you learnt about a business's customers and economics to shape your analysis.
AI leverage. You've used AI to produce work you couldn't have delivered without it – a prototype, an automation, or a modelling pipeline others use. You judge where it helps and where it doesn't, and you check its output before anyone acts on it.
Communication. You write and speak clearly, directly, and concisely. You adapt technical detail for non-technical colleagues in commercial and strategy roles.

Bonus:

Lending experience. You've worked on credit risk, pricing, or customer lifetime value in lending or financial services.
Small-sample modelling. You've worked with scarce data and made principled choices about regularisation or sequential updating.
Production machine learning. You've taken models from exploration through to deployment and monitoring.
Coding in Python. The team works in Python and you’ll write analyses and code in that language.

The salary

We expect to pay from £80,000 to £100,000 for this role. But, we’re open-minded, so definitely include your salary goals with your application. We routinely benchmark salaries against market rates, and run quarterly performance and salary reviews.

The 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. We provide the infrastructure where every iwocan always has access to the best models and where those models have access to all of our data. We will help our people to learn how to use and grow with the new tools available to them.

The offices

We put a lot of effort into making iwoca a great place to work:

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.

The benefits

Flexible working hours.
Medical insurance from Vitality, including discounted gym membership.
A private GP service (separate from Vitality) for you, your partner, and your dependents.
25 days’ holiday per year, an extra day off for your birthday, the option to buy or sell an additional five days of annual leave, and unlimited unpaid leave.
A one-month, fully paid sabbatical after four years.
Instant access to external counselling and therapy sessions for team members that need emotional or mental health support.
3% Pension contributions on total earnings.
An employee equity incentive scheme.
Generous parental leave and a nursery tax benefit scheme to help you save money.
Electric car scheme and cycle to work scheme.
Two company retreats a year: we’ve been to France, Italy, Spain, and further afield.