Head of Detection (Real-Time Intelligence & Defense Systems)
Job Description

Role Summary
We are seeking a Head of Detection to design and lead our real-time intelligence layer, responsible for identifying critical risks, anomalies, and opportunities across large-scale, fast-moving systems. This role leverages data, systems thinking, and AI to detect meaningful signals from vast, noisy, and seemingly unrelated data sources—enabling rapid downstream decision-making and automated action. You will be a core architect of our Defense Flywheel: Data → Signal → Decision → Action → Learning.

Key Responsibilities
- Build a Unified Detection System:
- Design detection frameworks across client behavior, system anomalies, human/operator anomalies, product & PnL irregularities, and cross-domain patterns.
- Integrate multi-source data into a unified detection layer, including trading/activity logs, system metrics, user behavior, and financial outcomes.

- Extract Signal from Noise (Core Mission):
- Develop systems to identify non-obvious patterns across datasets.
- Detect early weak signals and correlate multi-dimensional anomalies into actionable insights.
- Build signal scoring frameworks to ensure output is actionable, high-confidence, and decision-ready.

- Real-Time Detection Architecture:
- Design and deploy low-latency detection pipelines.
- Implement event-driven processing, streaming data systems, and real-time alerting frameworks.
- Ensure high coverage, high reliability, and minimal detection delay (seconds-level).

- AI & Model Integration:
- Lead development of anomaly detection models, behavioral clustering, and pattern recognition systems.
- Develop hybrid rule + ML detection frameworks.
- Apply AI to reduce noise, improve precision, and discover hidden relationships.

- Continuous Learning & Feedback Loop:
- Build self-improving detection systems: incident → root cause → model refinement.
- Own incident replay systems, pattern libraries, and model retraining pipelines.

- Cross-Functional Signal Integration:
- Partner with data engineering, infrastructure/system teams, risk/operations/trading.
- Ensure detection logic reflects real-world system behavior.

- Build & Lead Detection Team:
- Hire and lead detection engineers, applied data scientists, and behavioral analysts.
- Shift team mindset from “Monitoring & reporting” to “Real-time signal engineering”.

Required Skill Sets
- 8–15+ years in real-time data systems, fraud detection/risk analytics, large-scale monitoring, AI/ML in production, distributed systems/platform engineering.
- Experience with real-time anomaly detection platforms, monitoring systems at scale, high data volume, high noise, and high cost of delayed detection.
- Systems Thinking: Ability to understand complex systems, cross-domain dependencies, and connect unrelated signals.
- Data & Real-Time Processing: Experience with streaming systems (Kafka, Flink, Spark Streaming), event-driven architectures, and large-scale pipelines.
- Applied AI / Detection Models: Expertise in anomaly detection, pattern recognition, behavioral analytics, and real-time deployment.
- Signal Engineering: Ability to filter noise, design scoring, define dynamic thresholds, and prioritize signals.
- Problem Decomposition: Skill in breaking down complex problems into structured detection logic and operating with incomplete information.
- Fluency in both English and Chinese (Mandarin) is required for effective cross-regional communication and collaboration.