Lead Credit Risk Data Scientist

About the role

You will own the end-to-end lifecycle of Billie's credit risk data science initiatives, translating ambiguous business challenges into scalable machine learning solutions. You serve as the domain authority for credit scoring, portfolio management, and decision engine optimization, ensuring models directly improve profitability and risk-adjusted returns. You will partner deeply with Engineering, Product, and fellow Data Scientists to embed risk thinking into the core of Billie's technology and product decisions. Your work will push the boundaries of applied AI by exploring and productionizing advanced techniques such as LLMs, RAG, and AI agents for credit problems. You will define hypotheses, run rigorous experiments, and synthesize complex results into clear, actionable strategies for the business. Based in Berlin, this senior leadership role requires a strong product mindset and the ability to drive initiatives forward with minimal direction. You will mentor junior scientists and shape the technical roadmap, ensuring credit risk models are robust, scalable, and aligned with Billie's growth ambitions.

Key facts

What you'll do

• Take ownership over one of the most important KPIs leading to Billie's success, directly impacting our P&L with your expertise.
• Drive the technical solution and execution of high-quality, impactful ML solutions across multiple domains within the Data Science team, ensuring project success from conception to production.
• Identify and apply advanced AI methodologies to push Billie's credit scoring capabilities beyond conventional approaches, turning emerging techniques into production-ready solutions (e.g. LLMs, RAG, AI agents, foundation models).
• Apply exceptional hands-on expertise in quantitative analysis, data mining, data science, and advanced ML to model complex business patterns, build state-of-the-art credit risk models (PD, LGD, EAD, etc), 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.
• Partner closely with Engineering, Product, and Data Science teams to enhance and optimize the decision engine, improving its logic, integrating new data sources, and enhancing functionalities.
• Mentor and grow junior Data Scientists within the team, and bring a technical perspective to system design discussions across backend and ML, ensuring credit risk solutions are built for scale from the ground up.
• Own the end-to-end data science workflow, including data validation, feature engineering, model development, rigorous testing, and continuous monitoring in production environments.
• Translate evolving business requirements into robust analytical strategies, ensuring that risk models remain aligned with product goals and regulatory expectations.
• Contribute to the definition of the technical roadmap, prioritizing initiatives that balance impact, feasibility, and long-term maintainability.

Requirements

• 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 proficiency in Python (pandas, scikit-learn, XGBoost, PyTorch/TensorFlow) and SQL (Snowflake, BigQuery, etc.), and experience with data visualization tools like Tableau.
• Hands-on experience working with LLMs and generative AI, with the ability to evaluate, integrate, and fine-tune models in a production environment.
• 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.
• Hands-on experience with graph databases (e.g. Neo4j) to model, analyze, and extract features from highly interconnected data is also highly desired.
• Strong business acumen and the ability to translate complex business problems into clear analytical and technical requirements that deliver maximum value.
• Excellent communication and data storytelling skills, with a track record of maximizing the impact of technical findings on organizational decision-making.
• 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.
• Fluency in English is required; German language skills are a plus for collaboration in our Berlin office.

Nice to have

• Experience with large-scale data platforms and streaming data architectures.
• Knowledge of regulatory and compliance considerations for credit risk modeling in European markets.
• Background in fintech or financial services environments.

Practical notes

This is a full-time position based in Berlin. We are a deep-tech company building financial products, and you will work closely with cross-functional teams to deliver scalable, production-grade solutions. The role involves significant responsibility and ownership from day one, with direct impact on company performance.