Data Scientist (Fraud)

Responsibilities

• As a Fraud Data Scientist, you will be a core technical contributor within Billie’s Decision Science group.

• You will design and build robust, scalable machine learning solutions that prevent fraud, with a direct and measurable impact on Billie’s bottom line.

• You will own the end-to-end modeling lifecycle: defining the analytical approach, testing hypotheses, and deploying models that capture complex debtor behavior and emerging fraud patterns

• Design and ship anti-fraud models, taking ownership of project priorities and delivering production-ready solutions

• Model debtor behavioral patterns, identify risk factors, and optimize the logic of Billie’s real-time decision engine using quantitative analysis, data mining, and advanced ML

• Balance precision and recall under severe class imbalance, explicitly weighing the cost of false positives (customer friction) against missed fraud (financial loss)

• Monitor deployed models for drift and adversarial adaptation, and retrain or recalibrate as fraud patterns shift

• Collaborate with data and software engineers, analysts, and product managers to improve decision logic, integrate new data sources, and extend system functionality

• Own the deployment and operationalization of ML services within real-time latency constraints, working with Engineering on infrastructure requirements such as containerization and event-driven architectures

• Share knowledge across the team and contribute to strong experimentation and coding practices

• Turn technical findings into clear, actionable recommendations through effective data storytelling for both technical and non-technical stakeholders

Benefits

• Flexibility first: We come to the office for important events, but do not have a permanent office presence. Our teams decide how to get the best results

• 30 days vacation per year and 5 extra paid days to look after sick children

• Virtual Share Program

• German language courses from different language levels free of charge

• One-off relocation bonus to make your move easier

• Reimbursement of travel expenses and discount on local public transport in Berlin with the BVG

• Individual training budget of 1,000 euros for courses, conferences and more every year

• Free gym access, weekly yoga classes, healthy snacks, cereals, drinks and more

Qualifications

• Sharp problem-solving skills, with the ability to translate complex business challenges into clean, efficient, and scalable technical requirements

• Deep expertise in classification models (classical and deep learning), anomaly detection, and graph-based methods (e.g., graph neural networks, entity-link analysis)

• Proven ability to manage stakeholders across technical and non-technical functions, aligning technical roadmaps with business priorities

• Proven advanced proficiency in Python (e.g. pandas, scikit-learn, xgboost) and SQL (Snowflake, Postgres, or MySQL)

• Strong communication skills, with a track record of using data to influence strategy and drive cross-functional engagement

• 3-5+ years in a quantitative or machine learning role, ideally in fintech or another high-transaction environment. Direct experience in fraud prevention or risk modeling is strongly preferred

• Hands‑on experience productionizing ML services, with a strong grasp of modern MLOps concepts such as containerization (Docker/Kubernetes) and event-driven architectures

• Experience with ML orchestration frameworks such as Metaflow, Apache Flink, or similar MLOps tooling

• Experience implementing LLM-based workflows (e.g., agentic pipelines, retrieval-augmented generation, or LLM-assisted feature extraction), particularly applied to fraud detection or risk signals