Data Scientist - Fraud Decisioning

Reposted 14 Days Ago
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London, Greater London, England
In-Office
Mid level
Financial Services
The Role
As a Data Scientist focused on Fraud Decisioning, you will analyze data to create and optimize fraud management models and strategies, balance loss reduction with approvals, and monitor model performance.
Summary Generated by Built In

Who are Liberis?


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/.


The team 

We are the Risk Analytics team with 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. 

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! 

 

The role  

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.
  • SQL proficiency for data extraction; strong Excel for quick analysis. 
  • 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.


 #LI-CG1 

Top Skills

Excel
GCP
Git
Looker
Power BI
SQL
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The Company
HQ: London
216 Employees
Year Founded: 2007

What We Do

Liberis builds flexible embedded finance solutions that empower businesses and provide positive impact.

Founded in 2007, Liberis is a leading global embedded finance platform with a mission to provide small businesses with accessible and responsible finance, based on the belief that funding should always be a positive force for small businesses.

Liberis provides partners with the technology platform and financial solutions to offer hyper-personalised and accessible funding, empowering their small business customers to grow their revenues.

With over 14 global strategic partners and direct reach to more than 1 million small businesses, Liberis has provided nearly $1bn of funding in over 45,000 transactions, enabling over 100,000 jobs to be created and saved. Liberis’ revenue-based finance is a form of receivables finance, not a loan. Amounts advanced are subject to status and our underwriting process before any offer can be made.

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