Data Scientist - Fraud DecisioningLiberis • City of London, England, United Kingdom
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Data Scientist - Fraud Decisioning
Liberis
- City of London, England, United Kingdom
- City of London, England, United Kingdom
À propos
Liberis 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.
Some key info for you: Founded in the UK in 2007
Diverse team of over 260 talented people from more than 27 nationalities
Six offices across the globe: London, Nottingham, Mumbai, Atlanta, Munich & Stockholm
Named as one of FinTech’s Finest 50 by Welcome to the Jungle
Provided over $3bn in funding to small businesses so far!
The team We are the Risk Analytics team, focused on delivering high‑quality fraud management for our customers worldwide through advanced statistical analytics.
The role Are you energized 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 with large, multi‑source datasets, run A/B and champion‑challenger tests, and turn analytics into clear, deployable decision logic.
What You’ll Be Doing
Own global fraud decisioning: rules, thresholds, step‑up controls optimized for loss 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 (2‑4 years is a rough guide).
Up‑to‑date awareness of emerging fraud trends and the latest controls, 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; 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 you fit our exact criteria, apply anyway – we can arrange a call to see if the role is a fit. Humility is a wonderful thing, and we are interested in hearing 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, ideally 4 days. We embrace flexibility as a core part of our culture while valuing time spent together in the office.
Referrals increase your chances of interviewing at Liberis by 2x
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Compétences linguistiques
- English
Avis aux utilisateurs
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