Lead Credit Risk Data Scientist

Join Billie, a fast-growing fintech company, as a Lead Credit Risk Data Scientist. In this senior technical leadership role, you will be responsible for the end-to-end design, development, and productionization of machine learning solutions for credit and portfolio management. You will work closely with Engineering, Product, and Data Science teams, and have a direct impact on Billie's P&L. The position offers flexibility, 30 days of vacation, a virtual share program, and various other benefits.

• Leitung des gesamten Prozesses von der Konzeption bis zur Produktion von robusten, skalierbaren maschinellen Lernlösungen im Bereich Kredit- und Portfoliomanagement.
• Identifizierung und Anwendung fortschrittlicher KI-Methoden zur Verbesserung der Kreditbewertungsfähigkeiten von Billie, einschließlich der Nutzung neuer Techniken.
• Mentoring und Entwicklung von Junior Data Scientists im Team sowie technische Perspektive in Systemdesign-Diskussionen einbringen.
• This role requires a deep understanding of the business and the ability to apply your expertise to the most pressing challenges, driving a direct and measurable impact on Billie's P&L
• A strong product mindset: you're comfortable owning a roadmap, making trade-offs under uncertainty, and driving initiatives forward with minimal direction, translating business ambition into a clear technical plan
• Hands-on proficiency in Python (pandas, scikit-learn, XGBoost, PyTorch/TensorFlow) and SQL (Snowflake, BigQuery, etc.), and experience with data visualization tools like Tableau
• Proven experience leading the deployment and productionization of ML services, demonstrating a deep understanding of modern MLOps concepts like containerization (e.g., Docker, Kubernetes), event-driven architectures, and model monitoring
• 6+ years of Data Science experience, with significant exposure to the credit domain and deep expertise in PD modeling: from scorecard development and model validation through to production monitoring. Broader experience with LGD, EAD, limit policies, and portfolio management is strongly preferred
• Hands-on experience working with LLMs and generative AI, with the ability to evaluate, integrate, and fine-tune models in a production environment
• Hands-on experience with graph databases (e.g., Neo4j) to model, analyze, and extract features from highly interconnected data is also highly desired
• Excellent communication and data storytelling skills, with a track record of maximizing the impact of technical findings on organizational decision-making
• Strong business acumen and the ability to translate complex business problems into clear analytical and technical requirements that deliver maximum value