Staff Data Scientist

Job Responsibilities

• Lead high-impact machine learning and feature-development initiatives across device, network, browser, mobile, session, and behavioral intelligence.
• Own ambiguous fraud and identity risk problems where data quality, label reliability, adversarial behavior, customer impact, and product tradeoffs must be evaluated together.
• Develop production risk signals and models that balance fraud detection, false-positive risk, coverage, latency, explainability, robustness, and operational maintainability.
• Build and guide scalable feature-engineering approaches for high-cardinality, sparse, noisy, and platform-dependent telemetry.
• Investigate complex signal patterns such as spoofing, emulator behavior, automation, proxy/VPN usage, low-entropy fingerprints, telemetry gaps, device fragmentation, and over-linkage risk.
• Define evaluation methods for Digital Intelligence signals, including holdout design, leakage checks, drift monitoring, adversarial robustness, customer impact analysis, and long-term signal stability.
• Influence telemetry collection, data contracts, feature logging, model monitoring, and production readiness in partnership with engineering, product, risk, and platform teams.
• Translate open-ended product, customer, and fraud-risk questions into clear data science approaches, measurable hypotheses, and production-ready signal roadmaps.
• Raise team standards for feature quality, model validation, explainability, documentation, and risk-signal governance.
• Communicate technical recommendations, tradeoffs, limitations, and results clearly to data science peers, engineering partners, product stakeholders, risk teams, and senior leadership.
• Mentor data scientists by improving problem framing, modeling judgment, validation rigor, code quality, and ability to operate independently in ambiguous domains.

Job Requirements

• Master’s or Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, Data Science, or a related quantitative field.
• 12+ years of experience in data science, applied machine learning, statistical modeling, or related technical roles.
• Significant experience building, deploying, validating, and improving production machine learning models, risk signals, or decisioning systems.
• Strong background in fraud detection, identity verification, trust and safety, anomaly detection, cybersecurity, risk modeling, or another adversarial data domain.
• Expert-level SQL skills and extensive experience working with large-scale, complex, noisy datasets.
• Strong proficiency in Python and distributed data processing frameworks such as Spark, PySpark, or equivalent tools.
• Deep understanding of supervised learning, unsupervised learning, anomaly detection, feature engineering, model evaluation, production monitoring, and statistical validation.
• 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.
• Experience influencing data architecture, instrumentation, feature logging, and product direction through technical credibility rather than direct authority.
• Excellent communication skills, including the ability to explain complex data science decisions and risk tradeoffs to technical and non-technical audiences.
• Strong mentorship skills and a track record of improving the technical quality and judgment of other data scientists.

Preferred Qualifications

• 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.
• Hands-on experience with ML frameworks such as scikit-learn, XGBoost, TensorFlow, PyTorch, or similar.
• Experience setting standards for model explainability, feature governance, validation methodology, or production ML observability.