Staff Data Scientist (Digital Intelligence)

Join our Digital Intelligence team as a Staff Data Scientist. In this hands-on technical leadership role, you will turn high-scale telemetry into production-grade fraud and identity risk signals. You will lead machine learning and feature-development initiatives, develop production risk signals and models, and influence telemetry collection and data contracts. You will also mentor data scientists and communicate technical recommendations to various stakeholders. Enjoy comprehensive benefits, including health coverage, generous parental leave, and a 100% remote work environment.

• Lead the development of production-grade fraud and identity risk signals by turning high-scale telemetry into actionable insights.
• Define rigorous evaluation methods and influence telemetry collection to improve the quality and reliability of data used for fraud detection.
• Mentor and guide data scientists in improving their technical skills, problem framing, modeling judgment, and ability to operate independently.
• 12+ years of experience in data science, applied machine learning, statistical modeling, or related technical roles
• Experience influencing data architecture, instrumentation, feature logging, and product direction through technical credibility rather than direct authority
• Strong mentorship skills and a track record of improving the technical quality and judgment of other data scientists
• Master’s or Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, Data Science, or a related quantitative field
• Excellent communication skills, including the ability to explain complex data science decisions and risk tradeoffs to technical and non-technical audiences
• Strong background in fraud detection, identity verification, trust and safety, anomaly detection, cybersecurity, risk modeling, or another adversarial data domain
• Deep understanding of supervised learning, unsupervised learning, anomaly detection, feature engineering, model evaluation, production monitoring, and statistical validation
• Strong proficiency in Python and distributed data processing frameworks such as Spark, PySpark, or equivalent tools
• Expert-level SQL skills and extensive experience working with large-scale, complex, noisy datasets
• Demonstrated ability to work with imperfect labels, delayed outcomes, telemetry artifacts, instrumentation gaps, and changing fraud patterns
• Strong judgment across data quality, modeling approach, feature design, explainability, operational complexity, and business impact
• Significant experience building, deploying, validating, and improving production machine learning models, risk signals, or decisioning systems
• Experience with device intelligence, browser/mobile fingerprinting, behavioral biometrics, network intelligence, VPN/proxy detection, entity resolution, or graph-based risk signals
• Experience designing features from high-cardinality categorical data using techniques such as aggregation, frequency encoding, target encoding, embeddings, graph features, or representation learning
• Experience with streaming, near-real-time, or low-latency decisioning systems
• Familiarity with adversarial modeling, robust ML, privacy-preserving ML, interpretable ML, or responsible AI practices
• Experience setting standards for model explainability, feature governance, validation methodology, or production ML observability
• Hands-on experience with ML frameworks such as scikit-learn, XGBoost, TensorFlow, PyTorch, or similar