Senior Data Scientist - Credit Risk Modelling

Senior Data Scientist - Credit Risk Modelling Hybrid in London or remote in the UK We’re looking for a Senior Data Scientist to join our Credit Risk Modelling team. You'll shape how iwoca models credit risk – setting the technical direction on multi-quarter projects, and lifting the bar for the team as you go. 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. Some work on auto-space models that decide within five minutes from data candidates authorise us to pull. Others work on manual-space models that pick up when a credit analyst adds digital footprint and income data, which lands a decision within 24 hours. The role You'll own credit and CLtV modelling projects end to end, from spotting where the modelling stack is holding the business back through to landing the change. 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. • Production ML. You have built and shipped supervised ML models end to end – exploration, training, deployment, monitoring. • Research mindset. You proactively explore 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. You've influenced technical direction beyond your own projects. • Project leadership. You've owned modelling projects end to end, from spotting the opportunity through framing, method choice, shipping, and landing the commercial impact. You move f