Senior Data Scientist - Digital Intelligence, Device Signals

What You'll Do

• Design and deploy advanced machine learning systems for device identification, anomaly detection, and fraud prevention—balancing precision, recall, and real-world adversarial dynamics.
• Contribute to the development of scalable data pipelines and production ML workflows using structured and unstructured telemetry (e.g., browser, mobile, session data).
• Investigate high-complexity signals (e.g., emulator use, spoofing, low-entropy fingerprints), applying advanced statistical methods and domain knowledge to detect fraud and abuse.
• Translate ambiguous business problems into modeling approaches, using a combination of supervised, unsupervised, and heuristic techniques.
• Partner with engineering, product, and risk teams to contribute to data architecture decisions, signal collection, and planning.
• Drive experimental design, A/B testing frameworks, and robust validation techniques to ensure model generalizability and long-term trust.
• Contribute to team standards for ML explainability, risk evaluation, and feature logging.
• Document methodologies and communicate results effectively through dashboards, presentations, and reports for both technical and executive audiences.
• Mentor junior data scientists and participate in cross-functional working groups.

What You Bring

• Master’s degree (or equivalent practical experience) in Computer Science, Machine Learning, Statistics, or a related quantitative field.
• 6+ years of experience in data science or applied machine learning, including experience working in production environments.
• Excellent SQL skills and extensive experience with large-scale databases and data modeling.
• Proven track record of deploying and maintaining ML models in live systems, ideally involving streaming or near-real-time data.
• 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.
• 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.
• Demonstrated ability to break down ambiguous problems, apply analytical rigor, and uncover meaningful insights that influence product or risk strategies.
• Strong judgment across data quality, model selection, and business impact tradeoffs.
• Collaborative mindset and experience working cross-functionally with product, engineering, and analytics teams.

Preferred Qualifications

• Background in fraud detection, behavioral biometrics, anomaly detection, or adversarial modeling.
• Experience with high-cardinality feature engineering techniques (e.g., frequency/target encoding, embeddings).
• Familiarity with privacy-preserving or robust ML techniques.
• Knowledge of browser/mobile fingerprinting, VPN/proxy detection, or telemetry signal processing.