Detection Expert
About the Role
We are looking for a Detection Product Expert to own and drive the design, development, and continuous improvement of our real-time detection capabilities. This is a senior individual contributor 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 not manage a team directly, but your work will have direct impact on the integrity and safety of our platform.
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.
- 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.
- Proficiency in Python and SQL; 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.
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