Data Scientist

Data Scientist

Crypto-native iGaming operator ·Hybrid · Competitive base + strong growth potential

THE OPPORTUNITY

Data science here isn't a support function running dashboards for someone else's decisions — it's a first-class discipline that shapes how the business grows, prices risk, and protects players. We're a fast-growing crypto-native casino scaling across multiple licences and player bases, and we want a Data Scientist who can turn messy real-world player behaviour into models that actually ship.

Fraud, AML, and player-risk are where this role bites hardest. Every model you deploy — a new anomaly detector, a smarter collusion classifier, a sharper bonus-abuse score — feeds straight into the real-time decision engine and shows up in the fraud dashboard the next morning. Small team, senior audience, no six-month roadmap wait.

WHAT YOU'LL ACTUALLY DO

• Build fraud and AML models end-to-end — feature engineering, training, validation, deployment, and monitoring in production

• Design anomaly-detection and behavioural-clustering models to surface bonus abuse rings, multi-accounting, chip-dumping, and collusion in live casino and sportsbook

• Model chargeback risk, deposit-withdrawal anomalies, and player-lifecycle risk across crypto and fiat rails

• Work directly on crypto wallet flows and on-chain behaviour — mixers, hot/cold wallet patterns, high-risk jurisdictions — alongside our Chainalysis / TRM / Elliptic tooling

• Partner with the fraud analysts and rules engineers so your model outputs become tunable rules with clear false-positive and precision targets

• Own the experimentation framework for risk models — champion/challenger, backtesting, drift monitoring, and honest post-deployment reporting

• Collaborate with Payments, VIP, and Product on friction-vs-risk trade-offs — honest players don't get blocked, and the model tells you why

• Bring the story — visualisations and readouts in Looker or Metabase that make model behaviour legible to non-technical leadership

WHO YOU ARE

• A relevant degree in Data Science, Mathematics, Statistics, Computer Science, Physics, or a closely related quantitative discipline — this is a non-negotiable for the role

• 3-6 years of hands-on data-science experience, ideally with meaningful exposure to fraud, risk, payments, or AML modelling in iGaming, fintech, payments, or challenger banking

• Strong Python and SQL — you can go from a fresh dataset to a validated model without waiting on anyone else

• Deep familiarity with the modelling toolkit for fraud: gradient boosting, isolation forests, graph-based collusion detection, embedding methods, and sensible use of deep learning where it earns its keep

• Genuine understanding of imbalanced-class problems, precision-recall trade-offs, and the operational cost of a false positive on a real player

• Comfort with production data science — model registry, feature store, CI/CD for models, monitoring, and clean handovers to engineering

• You understand crypto payment flows — on-chain vs off-chain, wallet clustering, exchange deposits — or you're hungry to get there fast

• Scale-up-ready: shifting priorities, small team, direct exposure to leadership, no hiding behind a Jira column

NICE TO HAVE (NOT REQUIRED)

• MSc or PhD in a quantitative discipline

• Prior work on real-time inference systems and low-latency feature pipelines

• Graph neural networks or link-analysis experience applied to collusion / fraud rings

• Exposure to Chainalysis, TRM Labs, or Elliptic APIs and datasets

• Casino, sportsbook, or live-dealer fraud modelling experience

• Certified Fraud Examiner (CFE), ACAMS, or ICA Diploma in AML