Detection Lead/Detection Product Owner

About the Role We are looking for a Detection Product Owner to own and drive the design, development, and continuous improvement of our real-time detection capabilities. This is a senior role — you will be the subject matter expert and product owner for detection logic, sitting at the intersection of data engineering, AI/ML, and risk operations. You will own the daily execution and delivery of the Detection Squad, ensuring detection priorities are translated into actionable initiatives and continuously driven through execution. A key part of the role will be to build and evolve a detection product focused on identifying, analysing, and processing behavioural clusters and anomalies across the platform, turning complex signals and emerging patterns into scalable detection capabilities. What You Will Do Own the end-to-end detection product: define detection logic, signal scoring frameworks, and dynamic thresholds across client behaviour, system anomalies, and financial irregularities. Manage daily execution of the Detection Squad, driving prioritisation, delivery, and continuous improvement of detection initiatives and ensuring effective execution against detection objectives. Extract actionable signals from large-scale, high-noise, multi-source data — trading logs, user activity, system metrics, and financial outcomes. Design and optimise real-time detection pipelines with low latency requirements (seconds-level response for critical events). Develop and iterate on anomaly detection models, behavioural clustering, and pattern recognition systems; integrate hybrid rule-based and ML approaches. Build and maintain continuous feedback loops: incident → root cause analysis → model refinement → retraining pipelines. Translate complex detection requirements into precise technical specifications for engineering teams; act as the bridge between detection logic and production systems. Partner cross-functionally with data engineering, risk operations, trading, and infrastructure teams to ensure full coverage and system alignment. Drive reduction of false positives while maintaining high detection precision and coverage across key system and user activity. What We Are Looking For 5–10 years of experience in real-time detection systems, fraud/risk analytics, trade surveillance, or AI/ML in production environments. Hands-on experience building and owning detection or anomaly detection systems — not just contributing to them. Strong applied ML/AI capability: anomaly detection models, behavioural analytics, pattern recognition, real-time model deployment. Experience with streaming or event-driven data systems (e.g. Kafka, Flink, KDB/q, or equivalent). Systems thinking: ability to connect cross-domain signals, understand complex system dependencies, and decompose ambiguous problems into structured detection logic. Fluency in both English and Mandarin Chinese — required for effective cross-regional collaboration. Preferred Background in financial services, crypto exchange, fintech, or large-scale internet platform risk. Experience with graph-based detection, knowledge graphs, or fraud network analysis. Track record of measurable outcomes: reduction in false positives, detection latency improvements, or fraud loss reduction. Exposure to market manipulation detection, trade surveillance, or exchange-level risk monitoring.