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.