Senior Data Scientist
Role details
Job location
Tech stack
Job description
As a Senior Data Scientist for Fraud Prevention, you will be a key technical pillar within the broader Decision Science group. You will be responsible for designing and building robust, scalable machine learning solutions aimed at preventing fraud. A mission that has a direct and measurable impact on Billie's bottom line. You will own the end-to-end modeling lifecycle, from defining the analytical approach and testing hypotheses to deploying models that understand complex debtor behavioral patterns and emerging fraud trends.
In more detail, you will:
- Design and execute high impact anti-fraud solutions, taking full ownership of project priorities and ensuring the delivery of high-quality, production-ready models.
- Apply extensive expertise in quantitative analysis, data mining, and advanced ML to model debtor behavioral patterns, identify risk factors, and optimize Billie's real-time decision engine logic.
- Collaborate deeply within cross-functional teams of data and software engineers, analysts, and product managers to improve decision engine logic, integrate new data sources, and enhance system functionalities.
- Own the deployment and operationalization of ML services, working closely with Engineering to define requirements for robust infrastructure, including containerization and event-driven architectures.
- Act as a technical mentor to junior team members, fostering a culture of technical excellence, rigorous experimentation, and best-in-class coding standards.
- Maximize the impact of technical findings on critical business decisions through excellent data storytelling and clear, actionable recommendations for stakeholders.
Requirements
Do you have experience in Tableau?, * 5+ years of experience in a data-driven, quantitative, or machine learning role, ideally within fintech or a high-transaction environment.
- Advanced proficiency in Python (pandas, scikit-learn, xgboost) and SQL (Snowflake, Postgres, or MySQL), with a strong grasp of data visualization tools like Tableau.
- Deep technical expertise in general classification models (classical and deep learning), anomaly detection algorithms, and graph-based networks.
- Hands-on experience productionizing ML services, demonstrating a strong understanding of modern MLOps concepts such as containerization (Docker/Kubernetes) and event-driven architectures.
- Proven ability to manage stakeholders across both technical and non-technical functions, aligning technical roadmaps with business priorities.
- Sharp problem-solving capabilities 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 organizational strategy and drive cross-functional engagement.
- Experience with ML orchestration frameworks such as Metaflow, Apache Flink, or similar MLOps tooling.