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Berlin, Deutschland
Hybrid
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 Lead Data Scientist within the Credit Data 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 challenges, driving a direct and measurable impact on Billie's P&L. Reporting directly to the VP of Data Science and based in Berlin, this is a senior technical leadership role; you will own Billie's credit risk modeling domain end-to-end end-to-end, work in close partnership with Engineering, Product, and Data Science peers, and play a central role in shaping and executing on the roadmap for state-of-the-art ML models and applied AI that power our fast-growing business.
In more detail, you will:
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. You will be a key voice in technical discussions across team boundaries, ensuring credit risk thinking is embedded in how Billie builds its systems.
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.
What You Bring to the Team:
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.
What We Offer:
Challenging and impactful work that drives personal and professional growth
One of the best Virtual Shares Incentive Programs in the market, so that everyone at Billie is invested in our success
Flexible work hours and trust in your ability to deliver, empowering you to take control of your work-life balance
A hybrid working approach that allows you to work from home for up to 3 days per week
Enjoy 30 days vacation per year, sabbatical opportunities, and extra child sickness leave for parents.
Our “Catch a Ride with Billie” program that enables discounted access to Berlin Public Transport (BVG), Deutschland-Ticket, OR JobRad
A yearly development budget to broaden your skill set and horizons
Free German group classes
An English-speaking, multicultural team with more than 40 nationalities
Building meaningful connections with your colleagues through company and team events, interest groups, the Billie run club, game nights, and more, powered by our Formula Fun Team!
Billie is an equal opportunity employer and we do not discriminate on the basis of race, color, religion, sexual orientation, gender identity or expression, national origin, age, disability, or any other protected characteristic. We are committed to creating an inclusive environment where everyone feels they belong. All qualified applicants are welcome and we especially encourage you to apply, even if you don't check every box in the job description.
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