Data Scientist - Credit Risk Modelling
iwoca · London, United Kingdom · 21d ago
Hybrid in London or remote in the UK
We’re looking for a Data Scientist to join our Credit Risk Modelling team.
You'll work on the credit models that sit at the core of iwoca's lending business – the models that decide who we lend to, on what terms, and how far the product can grow.
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
The Credit Risk Modelling team owns credit risk and customer lifetime value (CLtV) modelling for iwoca's UK and German lending. That covers the probabilistic machine learning models behind every credit decision, plus the CLtV models that shape pricing and portfolio strategy.
The team is around twelve data scientists, who combined create models to efficiently drive fully automated and human-in-the-loop decision making. This forms the core analytical team that interface with and support smaller analytic functions in other parts of the business.
The role
You'll run credit and CLtV modelling projects alongside the rest of the team. The work spans keeping production models healthy, incremental development, and research that reshapes how the models work. AI has lowered the cost of prototyping enough that ideas which used to sit below the priority line are now viable, so the R&D share of the role is growing.
Live examples of the work:
Causal estimation of offer terms. Modelling how amount, duration, and price shape customer outcomes.
Unifying auto and manual models. The two families were built without forced technical alignment. Finding a principled way to unify them on a common cost function is open work.
IFRS accounting model. A multi-stage credit model where information propagates back from later-stage recovery predictions to sharpen upfront loss estimates.
Generalising credit and CLtV. Whether a more general framing could replace both separate models is an open research question.
The requirements
Essential:
Communication. You write and speak clearly, directly, and concisely. You adapt technical detail to your audience.
Statistical foundations. You have a background in probability and statistics from a quantitative field. You reason about uncertainty and calibration as first-order concerns.
Research mindset. You're actively exploring new ways to add value. R&D time is when you expect to find the next step change.
Judgement. You critically evaluate model output – yours, a colleague's, or an LLM's – and can explain why a choice is right. You defend your reasoning under challenge, and challenge others' when the evidence points elsewhere.
Analytical ownership. You take ambiguous problems end to end, from framing to a landed decision. You like to move fast, iterate, and update on new evidence rather than chase perfection.
AI fluency. You use AI as a primary tool. You prototype with it, automate with it, and take on R&D that would not otherwise be viable. You use judgement on where it helps and where it doesn't.
Bonus:
Domain experience. You have worked in credit risk, lending, or customer lifetime value modelling.
Production ML. You have built and shipped supervised ML models end to end – exploration, training, deployment, monitoring.
Non-linear methods. You can think in terms of the cost function and inductive biases of your models
Bayesian methods. You have used hierarchical models, MCMC, or Bayesian updating in real work.
Time series modelling. You have modelled temporal data where autocorrelation, drift, or seasonality mattered.
Python. The stack the team uses.
The salary
We expect to pay from £60,000 – £90,000 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.
And to make sure we all keep learning, we offer:
A learning and development budget for everyone.
Company-wide talks with internal and external speakers.
Access to learning platforms like Treehouse.
Useful links: