Fraud Data Scientist
Role details
Job location
Tech stack
Job description
As part of Decision Science, you will work on the models that decide, in milliseconds, whether a transaction is trustworthy. Your work directly shapes how much fraud we stop, how many good customers we approve, and how much risk the business carries., 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.
In more detail, you will:
- 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.
Requirements
Do you have experience in SQL?, Do you have a Master's degree?, * 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.
- Proven advanced proficiency in Python (e.g. pandas, scikit-learn, xgboost) and SQL (Snowflake, Postgres, or MySQL).
- Deep expertise in classification models (classical and deep learning), anomaly detection, and graph-based methods (e.g., graph neural networks, entity-link analysis).
- Hands-on experience productionizing ML services, with a strong grasp of modern MLOps concepts such as containerization (Docker/Kubernetes) and event-driven architectures.
- Proven ability to manage stakeholders across technical and non-technical functions, aligning technical roadmaps with business priorities.
- Sharp problem-solving skills, with the ability to translate complex business challenges into clean, efficient, and scalable technical requirements.
- Strong communication skills, with a track record of using data to influence strategy and drive cross-functional engagement.
Nice to have:
- 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.
Benefits & conditions
Pulled from the full job description
- Sabbatical
- Work from home
- Flexible schedule