Senior Data Scientist

About the role

Join Billie, a fast-growing fintech company, as a Senior Data Scientist focused on fraud prevention. In this role, you will design and build scalable machine learning solutions to prevent fraud, own the end-to-end modeling lifecycle, and collaborate with cross-functional teams. You will also act as a technical mentor to junior team members and maximize the impact of technical findings on critical business decisions. Enjoy benefits such as flexible work arrangements, 30 days of vacation, and an individual training budget. Key missions: Design and build robust, scalable machine learning solutions aimed at preventing fraud, owning the end-to-end modeling lifecycle.. Collaborate with cross-functional teams to improve decision engine logic, integrate new data sources, and enhance system functionalities.. Act as a technical mentor to junior team members, fostering a culture of technical excellence, rigorous experimentation, and best-in-class coding standards. Profile: - Sharp problem-solving capabilities with the ability to translate complex business challenges into clean, efficient, and scalable technical requirements - Advanced proficiency in Python (pandas, scikit-learn, xgboost) and SQL (Snowflake, Postgres, or MySQL), with a strong grasp of data visualization tools like Tableau - 3-5+ years years of experience in a data-driven, quantitative, or machine learning role, ideally within fintech or a high-transaction environment. Direct experience in fraud prevention, risk modeling, or a high-transaction fintech environment is highly preferred - Strong communication skills, with a track record of using data to influence organizational strategy and drive cross-functional engagement - Experience with ML orchestration frameworks such as Metaflow, Apache Flink, or similar MLOps tooling - Deep technical expertise in general classification models (classical and deep learning), anomaly detection algorithms, and graph-based networks - Hands-on experience productionizing ML services, demonstrating a strong understanding of modern MLOps concepts such as containerization (Docker/Kubernetes) and event-driven architectures - Proven ability to manage stakeholders across both technical and non-technical functions, aligning technical roadmaps with business priorities