Data Scientist - Fraud Decisioning London, United Kingdom in City of London

At Liberis, we are on a mission to unleash the power of small businesses all over the world - delivering the financial products they need to grow through a network of global partners. At its core, Liberis is a technology-driven company, bridging the gap between finance and small businesses. We use data and insights to help partners understand their customers’ real-time needs and tech to offer tailor-made financial products. Empowering small businesses to grow and keep their independent spirit alive is central to our vision. Since 2007, Liberis has funded over 50,000 small businesses with over $3bn - but we believe there is much more to be done.

The Risk Analytics team has a goal to drive intelligent decision-making by applying advanced statistical analytics to a wealth of data. At the heart of the Risk function, our focus is to deliver high-quality fraud management for our customers around the world. The Risk team is a global team with offices in London, Nottingham, and Atlanta US, covering Risk Analytics, Decision Analytics, Fraud Analytics, Underwriting, and Collections.

We are looking for a Fraud Model Developer to help us grow Liberis into the world’s leading embedded business finance provider!

Are you energised by complex problems, real autonomy, and the chance to innovate? If fraud management - and its constantly changing landscape - excites you, this is the role. Reporting directly to the Director of Risk Analytics, you’ll use deep data analysis to design, build, and productionise fraud strategies and models across the lifecycle balancing loss reduction with healthy approvals. You’ll work across large, multi-source datasets, run A/B and champion–challenger tests, and turn analytics into clear, deployable decision logic that moves the needle.

What you’ll be doing:

• Own global fraud decisioning: rules, thresholds, step-up controls optimised for £-EL reduction at stable approval rates.
• Build models end-to-end: problem framing, label/observation window design, sampling, feature engineering, training (logistic/GBM), calibration, back-testing, validation, documentation, and deployment into production decisioning.
• Experiment & ship: A/B and champion–challenger tests; cost-based optimisation; roll out winners quickly.
• Monitor & govern: Robust dashboards/alerts for model drift, PSI, stability, leakage, review yield, chargeback/refund ratios; publish a concise weekly fraud pack.
• Data & vendors: Evaluate new data sources and vendors, integrate where ROI is positive, and track performance over time.
• Cross-functional impact: translate analytics into clear policies/playbooks; work with Product/Engineering to land decision logic cleanly and safely.

What we think you’ll need:

• Experience in an analytical fraud management role with measurable impact (we expect this to be 2-4 years, as a rough guide).
• Up-to-date awareness of emerging fraud trends and the latest controls to manage them with a habit of turning intel into tests, rules, or model features quickly.
• Hands-on modelling experience: feature engineering and building/validating fraud models; understanding of ROC/PR curves, Gini/KS, calibration, stability.
• Ability to communicate clearly - turn complex analysis into crisp recommendations.
• Proactive, autonomous working style; you know when to dive deep and when to align stakeholders.
• Experience deploying models to production or translating models into rules/strategies in a decision engine.
• Experience with Power BI or Looker for reliable, self-serve dashboards.
• GCP exposure and familiarity with version control (Git) are a plus.
• A solid STEM background helps - but aptitude and impact matter most.

What happens next?

Think this sounds like the right next move for you? Or if you’re not completely confident that you fit our exact criteria, apply anyway and we can arrange a call to see if the role is fit for you. Humility is a wonderful thing, and we are interested in hearing about what you can add to Liberis!

Our hybrid approach:

Working together in person helps us move faster, collaborate better, and build a great Liberis culture. Our hybrid working policy requires team members to be in the office at least 3 days a week, but ideally 4 days. At Liberis, we embrace flexibility as a core part of our culture, while also valuing the importance of the time our teams spend together in the office.