Data Scientist (Fraud)

Billie; Berlin; Vor Ort; EUR 60.000 - 80.000; Vollzeit

Benefits dieser Stelle

30 days vacation per year

Relocation bonus

Individual training budget

Gym access and yoga classes

German language courses

Zusammenfassung

Billie in Berlin seeks a Fraud Data Scientist to drive the development of machine learning solutions aimed at fraud prevention. You will be pivotal in crafting scalable models, optimizing logic, and influencing strategy through data insights.

With a commitment to flexibility, Billie offers extensive vacation, a virtual share program, and a supportive work environment designed for collective success.

Qualifikationen

• 3-5+ years in a quantitative or machine learning role, ideally in fintech.
• Experience in fraud prevention or risk modeling preferred.
• Hands-on experience with MLOps concepts essential.

Aufgaben

• Design and build machine learning solutions for fraud prevention.
• Own the end-to-end modeling lifecycle and deploy models.
• Collaborate with engineers and product managers to enhance decision processes.

Kenntnisse

Problem-solving skills

Classification models

Python proficiency

SQL proficiency

Communication skills

Tools

Docker

Kubernetes

Metaflow

SQL (Snowflake, Postgres, MySQL)

Jobbeschreibung

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