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Data Scientist – Fraud, AML and Player Risk
ГибридПолная занятостьData ScienceGambling and CasinosFinancial Services
Join a fast-growing crypto-native iGaming operator as a Data Scientist focused on fraud, AML, player risk and real-time decisioning. You will turn player, payment and on-chain behavior into production models and explainable risk signals.
Обязанности
- Build fraud and AML models through feature engineering, training, validation, deployment and production monitoring
- Develop anomaly-detection and behavioral-clustering models for bonus abuse, multi-accounting, chip dumping and collusion
- Model chargeback risk, deposit and withdrawal anomalies, and player-lifecycle risk across crypto and fiat payment rails
- Analyze crypto wallet flows and on-chain behavior using relevant blockchain analytics tools
- Work with fraud analysts and rules engineers to convert model outputs into tunable rules with defined precision and false-positive targets
- Own experimentation for risk models, including champion-challenger testing, backtesting, drift monitoring and post-deployment reporting
- Partner with Payments, VIP and Product teams on friction-versus-risk decisions
- Create visualizations and reporting in Looker or Metabase for non-technical leadership
Требования
- Bachelor’s degree in data science, mathematics, statistics, computer science, physics or a related quantitative field
- Three to six years of hands-on data science experience
- Experience with fraud, risk, payments or AML modeling, ideally in iGaming, fintech, payments or banking
- Strong Python and SQL skills
- Experience with gradient boosting, isolation forests, graph-based detection, embeddings and appropriate use of deep learning
- Understanding of imbalanced-class problems, precision-recall trade-offs and false-positive costs
- Experience with production data science, including model registries, feature stores, model CI/CD and monitoring
- Understanding of crypto payment flows, including on-chain and off-chain activity, wallet clustering or exchange deposits, or the ability to learn quickly
Будет плюсом
- Master’s or PhD in a quantitative discipline
- Experience with real-time inference systems or low-latency feature pipelines
- Graph neural networks or link analysis for fraud detection
- Experience with Chainalysis, TRM Labs or Elliptic APIs and datasets
- Casino, sportsbook or live-dealer fraud modeling experience
- Certified Fraud Examiner, ACAMS or ICA Diploma in AML
Условия и преимущества
- Competitive base salary
- Strong growth potential
- Direct exposure to leadership
- Hybrid work arrangement