Fraud Data Scientist

We are Billie, the leading provider of Buy Now, Pay Later (BNPL) payment methods for businesses, offering B2B companies innovative digital payment services and modern checkout solutions. We are to create a new standard for business payments and have made it our mission to simplify the purchasing experience for all businesses making it a tool for growth. Our solutions are based on proprietary, machine-learning-supported risk models, fully digitized processes and a highly scalable tech platform. This makes us a deep-tech company building financial products, not the other way around. We love building simple and elegant solutions and we strive for automation and scalability. As part of Decision Science, you will work on the models that decide, in milliseconds, whether a transaction is trustworthy. Your work directly shapes how much fraud we stop, how many good customers we approve, and how much risk the business carries. About the role: 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. In more detail, you will: - 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. What you bring to the team: Must have: - 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. Nice to have: - Experience with ML orchestration frameworks such as Metaflow, Apache Flink, or similar MLOps tooling. - Experience implementing LLM-based workflows (e.g., agentic pipelines, retrieval-augmented generation, or LLM-assisted feature extraction), pa