About the job
Billie builds Buy Now, Pay Later (BNPL) solutions designed for B2B companies. The team aims to make business transactions simpler and more efficient, using proprietary machine learning risk models, digitized operations, and a scalable technology platform. Billie positions itself as a deep-tech company focused on seamless financial products for business clients.
Role Overview
The (Senior) Data Scientist for Fraud Prevention joins the Decision Science group in Berlin. This role centers on designing and deploying machine learning models to reduce fraud risk, directly supporting Billie's profitability. The work spans the full modeling lifecycle, from hypothesis testing to rolling out production models that analyze debtor behavior and spot new fraud patterns.
What You Will Do
- Develop and execute anti-fraud strategies, setting priorities to deliver high-quality, production-ready models.
- Apply expertise in quantitative analysis, data mining, and advanced machine learning to model debtor behavior, assess risk, and refine Billie's real-time decision engine.
- Work closely with data engineers, software engineers, analysts, and product managers to improve decision logic and bring in new data sources.
- Lead the deployment and operationalization of machine learning services, collaborating with Engineering to define infrastructure needs such as containerization and event-driven systems.
- Mentor junior team members, supporting a culture of technical rigor, experimentation, and strong coding standards.
- Translate technical findings into clear, actionable insights for stakeholders, using data storytelling to influence business decisions.
