Responsibilities
- Model Development: Prototype, evaluate, and help productionize machine learning models for fraud detection; own their ongoing monitoring and retraining cycles.
- Experimentation: Design and run experiments to measure the impact of fraud interventions, balancing customer experience against loss reduction.
- Risk Assessment: Size fraud typologies across our product lines to inform prioritisation and investment decisions.
- System Maintenance: Build and maintain anomaly detection systems to surface novel fraud vectors before they scale.
- Cross-Functional Collaboration: Work closely with fraud operations, engineers, product managers, and data analysts to translate model outputs into real-world mitigations.
Requirements
- A strong foundation in statistics with a degree in a quantitative field (Statistics, Mathematics, Engineering, Computer Science, or similar)
- 5+ 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
- 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
- Comfort working in fast-paced, cross-functional teams with high ownership expectations
Core Competencies
Demonstrates expertise in building and deploying machine learning models for fraud detection, with a strong foundation in statistics and data science fundamentals. Proven ability to collaborate cross-functionally and communicate technical insights effectively to drive actionable outcomes.
