Binance - Accelerator Program (Top Talent Program) – Data Scientist (AI Risk & Fraud Detection)
Requirements
• Currently enrolled in a Master's or PhD programme at a QS World University Rankings Top 50 university. • Majoring in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, Data Science, Financial Engineering or a closely related field. • Relevant hands-on experience in AI/ML, data analytics or risk management, through prior internships, work experience, research projects or competitions. • Strong foundations in machine learning and statistics, such as classification, anomaly detection, imbalanced learning, graph methods and model evaluation. • Strong passion for AI and a solid understanding of modern AI technologies such as LLMs, agents and deep learning, including their strengths and limitations. Hands-on experience applying AI to real problems, and an AI-native way of working that makes active use of AI tools in daily research and development. • Genuine interest in Web3 and the crypto industry, shown through projects, coursework, community involvement or personal use. Basic understanding of blockchain, exchanges or DeFi. • Excellent problem-solving skills, analytical rigour and the ability to work on ambiguous, adversarial problems where attackers keep adapting. • Strong communication and documentation skills. Able to explain technical findings clearly to both technical and non-technical stakeholders. • Bonus • Prior experience in fraud detection, anti-money laundering (AML), credit risk, cybersecurity or trust & safety, at a fintech, bank, payments company, exchange or large internet platform. • Hands-on experience with LLM techniques such as agent harness engineering and fine-tuning (e.g. SFT). • On-chain data analysis experience (e.g. address clustering, fund-flow tracing, smart-contract risk) or familiarity with Web3 security incidents and attack vectors. • Papers at top-tier conferences (NeurIPS, ICML, ICLR, KDD, WWW, CCS, USENIX Security, etc.), strong Kaggle or competition results, or notable open-source contributions. • Hackathon or personal project experience in AI × Risk or AI × Crypto.
Responsibilities
• Research, design and build ML/AI models to detect fraud and abnormal behaviour across the platform. Examples include account takeover, identity and KYC fraud, payment and deposit/withdrawal fraud, market abuse and abnormal trading, and coordinated or syndicated attacks. • Analyse large-scale transactional, behavioural, device and on-chain data to find emerging attack patterns, potential vulnerabilities and gaps in existing controls. • Explore how LLMs, agents, graph learning and anomaly detection can be used in risk scenarios, such as automated investigation, alert triage, risk reasoning and real-time monitoring. • Build fast prototypes (PoCs) to test new detection ideas, run rigorous offline and shadow evaluations, and deliver reproducible results. • Help move validated prototypes into production risk models, rules and monitoring tools, working with risk operations, engineering and security teams. • Track the latest research and industry incidents in AI, fraud detection and Web3 security, and turn the lessons into concrete, proactive risk control improvements.
Benefits
• Shape the future of finance • Collaborate with world-class talent in a user-centric global organization with a flat structure • Tackle challenging projects in a fast-paced and innovative enviornment with autonomy • Thrive in a results-driven workplace with opportunities for career growth and continuous learning • Competitive salary and company benefits • Remote-first, work-from-home arrangement (may vary depending on the nature of the role and team)
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