Risk Strategy Lead - iwocaPay at iwoca (London, United Kingdom)Risk Strategy Lead - iwocaPay

We're looking for a Risk Strategy Lead You'll lead the credit and fraud strategy for iwocaPay — using data and analysis to inform who we lend to, on what terms, and how we grow into new segments where historical data is limited. You'll steer modelling priorities, define what good looks like for the portfolio, and translate analytical trade-offs into commercial decisions for a cross-functional squad.

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 150,000 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.

iwocaPay is iwoca's business-to-business trade credit product. We help sellers get paid upfront while giving their buyers flexible payment terms, and in doing so, we now support more than 10,000 active trade accounts. The risk squad owns the trade-off at the heart of iwocaPay's product offering — using data to extend finance to the right customers, on the right terms, while ensuring customers and our book is adequately protected.

The squad has a solid baseline of controls in place and is now investing to build the analytical layer on top — predictive models, real-time monitoring, and a joined-up strategy across risk. This new role leads that shift, defining what the squad optimises for as the product scales into new segments and financing structures.

The squad sits within iwocaPay and also works with iwoca's Global Risk Modelling & Fraud team and the broader technical and data science functions.

• Decide what we optimise for, how we balance growth against loss, and how to move safely into new segments with limited data.
• Use data & analytics to inform credit policy and risk appetite, building for growth and not just loss control, and shaping approval rates, offer amounts and exposure.
• Weigh risk on both sides of the transaction: the buyers we extend credit to, and, where it applies, counterparty seller risk.
• Hold a consistent strategy across every route iwocaPay finances through, and extend it to new ones, protecting the book without adding friction to the customer experience.

• Set modelling priorities and own model governance alongside our data scientists, judging performance and feature quality rather than building them yourself.
• Turn analytical trade-offs into commercial decisions, owning the end-to-end strategy to present to Co-Leads and wider leadership.
• Define what "good" looks like for the portfolio: the metrics, thresholds, and monitoring that tell us whether our strategy is working or drifting.

INFLUENCE HOW RISK IS BUILT INTO THE PRODUCT

• Partner with product, analytics, operations and engineering to embed risk thinking early in new features, rather than retrofitting controls after launch.
• Give a cross-functional squad a clear direction and the context to do their best work — you're leading the strategy, not doing everything yourself.
• Work closely with iwoca's global risk & modelling teams to inform how our risk strategy and supporting models are built, tested, and iterated.

• Experience in or substantial exposure to delivering risk strategy in a digital lending, fintech, or payments context, built on automated, real-time decisioning rather than manual reviews.
• Strong analytical skills, with a background in mathematics, statistics, economics, or a related quantitative discipline, allowing you to challenge, tune, and evolve risk frameworks.
• Ability to monitor and evaluate automated machine learning risk models, assessing feature quality and guiding data scientists without necessarily needing to build models yourself.
• Ability to collaborate across engineering, data science, and product, translating analytical trade-offs into commercial decisions that non-specialists can act on.
• Uses AI tools to support analysis and decision making, applying strong judgement to ensure accuracy, risk awareness, and sound outcomes.

• We welcome candidates from product or strategy roles in payments, lending or finance who haven't owned risk directly but have been close to risk or financing decisions.
• Experience with fraud strategy alongside credit, ideally through transaction profiling rather than manual review.
• Familiarity with B2B or trade-credit dynamics, including seller counterparty risk.