AI Scientist - Anti-Fraud

Overview

Hytech is a leading management consulting firm headquartered in Australia and Singapore, specialising in digital transformation for fintech and financial services companies. We provide comprehensive consulting solutions, as well as middle- and back-office support, to empower our clients with streamlined operations and cutting-edge strategies.

With a global team of over 2,000 professionals, Hytech has established a strong presence worldwide, with offices in Australia, Singapore, Malaysia, Taiwan, Philippines, Thailand, Morocco, Cyprus, Dubai and more.

Introduction

We are seeking multiple experienced AI Scientists to build intelligent fraud detection and abuse prevention systems that secure our users and ecosystem. You will leverage the latest advancements in graph ML, transformers, and behavioral analytics to address some of the most critical threats today.

Responsibilities

• Design, build, and deploy AI models using transformer architectures, GNNs, and unsupervised learning for anomaly and abuse detection.
• Build scalable graph learning pipelines to detect communities of malicious actors and their on-chain behavioral patterns.
• Implement high-performance fraud scoring systems that can operate in real time at massive scale.
• Collaborate with campaign & marketing teams to identify abuse vectors in rewards, referral, and incentive systems.
• Conduct regular performance evaluations of ML models and continuously improve them using new research and adversarial learning techniques.
• Support fraud investigation workflows by providing interpretable model results and actionable insights.
• Stay current on the evolving threat landscape and proactively develop solutions against novel fraud patterns.

Qualifications

Must-Have

• Bachelor’s or Master’s degree in Computer Science, Data Science, or related field.
• 5+ years of hands-on experience in machine learning and fraud detection, ideally in fintech, e-commerce, or Web3.
• Strong background in:
• Graph machine learning, GNNs, and clustering algorithms
• Transformer models and attention mechanisms
• Fraud detection techniques, including anomaly detection and behavioral scoring
• Feature engineering from high-dimensional, noisy, and heterogeneous data sources
• Proficiency in Python and ML frameworks like PyTorch/TensorFlow.
• Familiarity with big data processing tools (Spark, Hive) and real-time data pipelines (Kafka, Flink).
• Solid understanding of EVM-compatible blockchain data (transactions, contract calls, logs) and wallet behaviors.

Preferred

• Experience with fraud rule engines or security scoring systems.
• Familiarity with tools like Chainalysis, Tenderly, Alchemy, or similar blockchain analytics platforms.