Moniepoint - Data Scientist (Fraud)
Requirements
• A strong foundation in statistics with a degree in a quantitative field (Statistics, Mathematics, Engineering, Computer Science, or similar). • 3+ years of experience in data science, decision science, or risk analytics within fraud, payments, or financial crime. • Hands-on experience building and deploying machine learning models in a production environment. • Fraud, risk, or financial services experience is a strong plus. • Solid grounding in data science fundamentals: experimentation, statistical inference, model evaluation, and feature engineering. • Comfort working in fast-paced, cross-functional teams with high ownership expectations. • Proficiency in Python and SQL; comfort working across the full model development lifecycle. • An investigative instinct — you enjoy digging into data to find patterns others miss. • The ability to communicate technical findings clearly to non-technical stakeholders and translate insights into action.
Responsibilities
• Prototype, evaluate, and help produce machine learning models for fraud detection; own their ongoing monitoring and retraining cycles. • Design and run experiments to measure the impact of fraud interventions, balancing customer experience against loss reduction. • Size fraud typologies across our product lines to inform prioritization and investment decisions. • Build and maintain anomaly detection systems to surface novel fraud vectors before they scale. • Work closely with fraud operations, engineers, product managers, and data analysts to translate model outputs into real-world mitigations. • Production-grade ML models and anomaly detection systems that effectively surface and mitigate novel fraud vectors before they scale. • Well-designed experiments that successfully balance customer experience against fraud loss reduction. • Clear sizing of fraud typologies that effectively drives product prioritization and strategic investment decisions. • Seamless cross-functional alignment where technical model outputs are consistently translated into real-world fraud mitigations.
Benefits
• Culture: We put our people first and prioritize the well-being of every team member. We've built a company where all opinions carry weight and where all voices are heard. We value and respect each other and always look out for one another. Above all, we are human. • Culture: • Learning: We have a learning and development-focused environment with an emphasis on knowledge sharing, training, and regular internal technical talks. • Learning: • Compensation: You'll receive an attractive salary, pension, health insurance, annual bonus, plus other benefits.
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