# Head of Detection Real-Time Intelligence & Defense Systems

> Fuku · Singapore, Singapore · Full-time · Posted 2026-08-26

**Workplace:** on_site

## Description

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.

## Apply

[Apply at Fuku](https://apply.workable.com/fuku/j/FA7DCD88B7/apply)

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