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Niche fraud-focused ML leadership role with specialized graph and adversarial expertise reduces qualified applicant density despite metro location.
Strong fraud, adversarial ML, graph learning and low-latency production experience limits cross-industry transferability.
Explicit 6+ years plus 2+ management, domain-specific fraud and MLOps requirements make filters stringent.
Own and drive the Data Science and ML roadmap for Arkose Titan's fraud detection and risk scoring products, aligning research with product priorities.
Lead development, deployment, and performance management of real-time ML models for fraud/risk decisioning at scale, establishing MLOps practices including model governance, monitoring, and retraining.
Manage and grow a small team of ML researchers; stay hands-on in model design, architecture, feature engineering, and code review.
6+ years experience building and deploying ML models in production; minimum 2 years managing ML engineers or data scientists.
Proven ability to ship ML systems impacting business metrics, preferably in fraud, trust & safety, cybersecurity, or adversarial data domains.
Strong technical expertise in classification, anomaly detection, graph-based, and sequence models, covering the full ML lifecycle (data pipelines to monitoring).
Work Experience Required: 6+ years ML production experience with at least 2 years people management experience.
Experienced leader combining technical depth with management of ML research teams in adversarial/imbalanced data environments like fraud or cybersecurity.
Strategic operator comfortable translating fraud/business metrics into ML objectives and collaborating cross-functionally with product and engineering.
Hands-on contributor skilled in modern ML lifecycle practices and MLOps, capable of driving innovation in graph ML and behavioral analytics related to fraud detection.