Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Scientist - Fraud Decisioning

Liberis
City of London
3 days ago
Create job alert

1 week ago Be among the first 25 applicants


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


#J-18808-Ljbffr

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.