Data Scientist - Fraud Decisioning London, United Kingdom

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. Learn more about Liberis by visiting https://www.liberis.com/ .

We are theRiskAnalyticsteamwith a goal to driveintelligent decision-makingbyapplying advancedstatisticalanalytics toa wealth of data.At the heart of the Risk function,our focus is to deliver high-qualityfraud managementforour customers around the world.

Risk team is a globally team with offices in London, Nottingham and Atlanta US, covers Risk Analytics, Decision Analytics, Fraud Analytics, Underwriting and Collections. We're on a mission to grow Liberis into the world's leading embedded business finance provider, and we're looking for a Fraud Model Developer to help us make that happen!

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.

Reportingdirectlyto the Director of RiskAnalytics,you’lluse deep data analysis to design, build, and productionise fraud strategies and models across the lifecyclebalancing loss reduction with healthy approvals.You’llwork across large, multi-source datasets, run A/B and champion–challenger tests, and turn analytics into clear, deployable decision logic that moves the needle.

• 
  
• 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 newdata 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.
  

• 
  
• 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.
  

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!

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.