About the role
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.
About
the Role: As a Senior Data Scientist within the Credit Decision Science team, you will serve as a domain expert responsible for the end-to-end design, development, and productionization of robust, scalable machine learning solutions for our credit and portfolio management domain. This role requires a deep understanding of the business and the ability to apply your expertise to the most pressing problems, driving a direct and measurable impact on Billie's key strategic goals. In more detail, you will: Drive the technical solution and execution of high-quality, impactful ML solutions across multiple domains within the Decision Science team, ensuring project success from conception to production. Apply exceptional hands-on expertise in quantitative analysis, data mining, data science, and advanced ML to model complex business patterns, identify risk factors, and optimize Billie's real-time decision engine logic for various use cases. Define and execute the analytics for complex, cross-domain problems, including developing hypotheses for experimentation, designing A/B tests, and synthesizing results into actionable insights. Serve as a technical leader and subject matter expert within cross-functional teams (Data/Software Engineers, Analysts, Product Managers, Business Leaders) to enhance and optimize the decision engine by improving its logic, integrating new data sources, and enhancing functionalities. What You Bring to