Data Scientist - Credit Risk Modelling

About the role

Data Scientist - Credit Risk Modelling 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.. 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: 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. Communication. You write and speak clearly, directly, and concisely. You adapt technical detail to your audience. 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 dat

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