Data Scientist, AI Risk & Fraud Detection

BinanceCryptocurrency

Location: Australia; Hong Kong; New Zealand; Singapore; TaiwanInternshipEntry

Salary not disclosed

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Job Details

Currently enrolled in a Master's or PhD programme at a QS World University Rankings Top 50 university.
Major in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, Data Science, Financial Engineering, or a closely related field.
Have hands-on experience in AI/ML, data analytics, or risk management through internships, work, research projects, or competitions.
Have strong foundations in machine learning and statistics, including classification, anomaly detection, imbalanced learning, graph methods, and model evaluation.
Understand modern AI technologies such as LLMs, agents, and deep learning, including their strengths and limitations; have applied AI to real problems.
Show genuine interest in Web3 and crypto, with a basic understanding of blockchain, exchanges, or DeFi.
Bring problem-solving skills, analytical rigour, and the ability to work on ambiguous, adversarial problems.
Have strong communication and documentation skills and be able to explain technical findings to technical and non-technical stakeholders.
Bonus: experience in fraud detection, AML, credit risk, cybersecurity, or trust and safety.
Bonus: hands-on experience with LLM techniques such as agent harness engineering or fine-tuning, or on-chain data analysis and Web3 security.

Research, design, and build ML/AI models to detect fraud and abnormal behaviour across the platform.
Analyse transactional, behavioural, device, and on-chain data to identify emerging attack patterns, vulnerabilities, and gaps in controls.
Explore LLMs, agents, graph learning, and anomaly detection for automated investigation, alert triage, risk reasoning, and real-time monitoring.
Build prototypes to test detection ideas and conduct offline and shadow evaluations with reproducible results.
Help move validated prototypes into production risk models, rules, and monitoring tools with risk operations, engineering, and security teams.
Track AI, fraud detection, and Web3 security research and incidents, and apply lessons to risk controls.

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