Senior Data Scientist (Digital Intelligence, Device Signals)

Join our Digital Intelligence team as a Senior Data Scientist. In this role, you will develop machine learning features and models to enhance fraud prevention and identity verification. You will work with high-volume data from various sources and contribute to the development of scalable data pipelines and production ML workflows. This position offers the opportunity to own impactful projects, collaborate cross-functionally, and deepen your expertise in applied ML for device and behavioral intelligence.

• Conduire le développement de fonctionnalités et de modèles d'apprentissage automatique pour la prévention de la fraude et la vérification d'identité.
• Collaborer avec des équipes interfonctionnelles pour contribuer aux décisions d'architecture des données, à la collecte des signaux et à la planification.
• Mentorer les data scientists juniors et participer aux groupes de travail interfonctionnels.
• Strong judgment across data quality, model selection, and business impact tradeoffs
• 6+ years of experience in data science or applied machine learning, including experience working in production environments
• Proven track record of deploying and maintaining ML models in live systems, ideally involving streaming or near-real-time data
• Excellent communication skills—able to explain complex technical results to non-technical stakeholders and senior leadership
• Experience designing and interpreting experiments, working with real-world noisy datasets, and applying sound validation techniques to assess model robustness
• Master’s degree (or equivalent practical experience) in Computer Science, Machine Learning, Statistics, or a related quantitative field
• Demonstrated ability to break down ambiguous problems, apply analytical rigor, and uncover meaningful insights that influence product or risk strategies
• Excellent SQL skills and extensive experience with large-scale databases and data modeling
• Collaborative mindset and experience working cross-functionally with product, engineering, and analytics teams
• Proficiency in Python and distributed computing tools (e.g., Spark, PySpark)
• Hands-on experience with ML frameworks such as scikit-learn, XGBoost, TensorFlow, or similar
• Background in fraud detection, behavioral biometrics, anomaly detection, or adversarial modeling
• Familiarity with privacy-preserving or robust ML techniques
• Knowledge of browser/mobile fingerprinting, VPN/proxy detection, or telemetry signal processing
• Experience with high-cardinality feature engineering techniques (e.g., frequency/target encoding, embeddings)