Role Overview
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. 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.
Responsibilities
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. Live examples of the work include: * Causal estimation of offer terms: Modelling how amount, duration, and price shape customer outcomes. * Unifying auto and manual models: Finding a principled way to unify them on a common cost function. * 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: Researching whether a more general framing could replace both separate models.
Salary and Culture
- Salary: £60,000 – £90,000.
- We routinely benchmark salaries against market rates, and run quarterly performance and salary reviews.
- 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.
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 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.
- 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, and access to learning platforms like Treehouse.
- Offices in London, Leeds, Berlin, and Frankfurt with drinks, snacks, and community-led groups.
Essential Skills
- 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.
- Judgement: You critically evaluate model output and can explain why a choice is right.
- Analytical ownership: You take ambiguous problems end to end, from framing to a landed decision.
- AI fluency: You use AI as a primary tool for prototyping, automation, and R&D.
Bonus Skills
- Domain experience: Credit risk, lending, or customer lifetime value modelling.
- Production ML: Experience building and shipping supervised ML models end to end.
- Non-linear methods: Ability to think in terms of the cost function and inductive biases of models.
- Bayesian methods: Experience with hierarchical models, MCMC, or Bayesian updating.
- Time series modelling: Experience modelling temporal data where autocorrelation, drift, or seasonality mattered.
- Python: The stack the team uses.
