Staff Data Scientist - Digital Intelligence

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.

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