Data Scientist, Billie

• 3-5+ years in a quantitative or machine learning role, ideally in fintech or another high-transaction environment. Direct experience in fraud prevention or risk modeling is strongly preferred
• Proven advanced proficiency in Python (e.g. pandas, scikit-learn, xgboost) and SQL (Snowflake, Postgres, or MySQL)
• Deep expertise in classification models (classical and deep learning), anomaly detection, and graph-based methods (e.g., graph neural networks, entity-link analysis)
• Hands-on experience productionizing ML services, with a strong grasp of modern MLOps concepts such as containerization (Docker/Kubernetes) and event-driven architectures
• Proven ability to manage stakeholders across technical and non-technical functions, aligning technical roadmaps with business priorities
• Sharp problem-solving skills, with the ability to translate complex business challenges into clean, efficient, and scalable technical requirements
• Strong communication skills, with a track record of using data to influence strategy and drive cross-functional engagement

Desirable

• As a Fraud Data Scientist, you will be a core technical contributor within Billie's Decision Science group.
• You will design and build robust, scalable machine learning solutions that prevent fraud, with a direct and measurable impact on Billie's bottom line.
• You will own the end-to-end modeling lifecycle: defining the analytical approach, testing hypotheses, and deploying models that capture complex debtor behavior and emerging fraud patterns
• Design and ship anti-fraud models, taking ownership of project priorities and delivering production-ready solutions
• Model debtor behavioral patterns, identify risk factors, and optimize the logic of Billie's real-time decision engine using quantitative analysis, data mining, and advanced ML
• Balance precision and recall under severe class imbalance, explicitly weighing the cost of false positives (customer friction) against missed fraud (financial loss)
• Monitor deployed models for drift and adversarial adaptation, and retrain or recalibrate as fraud patterns shift
• Collaborate with data and software engineers, analysts, and product managers to improve decision logic, integrate new data sources, and extend system functionality
• Own the deployment and operationalization of ML services within real-time latency constraints, working with Engineering on infrastructure requirements such as containerization and event-driven architectures
• Share knowledge across the team and contribute to strong experimentation and coding practices
• Turn technical findings into clear, actionable recommendations through effective data storytelling for both technical and non-technical stakeholders