Head of Detection Real-Time Intelligence & Defense Systems

Fuku · Singapore

Posted 8/26/2026 · last confirmed live 8/26/2026

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.

This role is published by Fuku on workable. SwiftFit is not the employer and does not accept applications.