Data Scientist, Risk (Machine Learning & Fraud Detection)

Responsibilities

• Feature Engineering & Data Infrastructure: Design and maintain scalable data pipelines (PB-scale) using technologies such as Spark, Hive, Flink, Trino, and Kafka. Collaborate with data engineers to build reusable, production-ready features for ML models and real-time decision engines.
• Fraud Group & Sybil Detection: Develop graph-based models and algorithms to detect coordinated fraud behavior using device data, IP addresses, fund flows, and user behavior. Design unsupervised clustering and rule-based systems to identify Sybil attacks and fraudulent account rings.
• User Behavior & Pattern Mining: Analyse large-scale user activity to identify behavioral anomalies such as automation, rapid transactions, or coordinated arbitrage activity. Train machine learning models for anomaly detection and integrate outputs into automated risk controls.
• On-Chain Data Intelligence: Conduct deep analysis of blockchain transaction data to cluster wallets, decode transactions, and identify suspicious smart contract patterns. Apply on-chain behavior modeling to detect malicious activity across addresses and platforms.
• Projects You May Work On: Building anomaly detection systems to stop automated bots and cross-account funding behaviors. Developing scalable ETL pipelines for real-time fraud scoring engines. Implementing graph algorithms to uncover hidden fraud rings within transaction and identity networks. Researching and prototyping on-chain Sybil scoring models using wallet clustering and contract analysis.

Requirements

• Minimum of 3 years of hands-on experience in developing machine learning models and building ML engineering solutions that drive tangible business outcomes.
• Strong expertise in user behavior modeling, fraud detection, graph analytics, or working with graph neural networks (GNNs).
• Proficient in unsupervised learning methods, including clustering, anomaly detection, and representation learning.
• Solid experience with on-chain data analysis, such as decoding blockchain transactions and clustering wallets based on behavioral and transactional patterns.
• Advanced programming skills in Python (required); familiarity with Scala or Java is a plus.
• Proven experience working with large-scale data processing frameworks and infrastructure, including Spark, Hive, Kafka, and Flink.
• Demonstrated success in deploying machine learning models or decision systems into production environments.
• Holds a Master’s degree in Data Science, Machine Learning, Computer Science, or a related field, or possesses equivalent practical experience.
• Comfortable working with large datasets at the terabyte to petabyte scale.
• Thrives in fast-paced, ambiguous, and early-stage (0 1) problem spaces with high ownership and initiative.
• Deep interest in fraud prevention, cryptocurrency risk, and graph-based intelligence.
• Excellent written and verbal communication skills, with the ability to clearly convey complex technical concepts in English to be able to coordinate with overseas partners and stakeholders.

• Shape the future with the world’s leading blockchain ecosystem
• Collaborate with world-class talent in a user-centric global organization with a flat structure
• Tackle unique, fast-paced projects with autonomy in an innovative environment
• Thrive in a results-driven workplace with opportunities for career growth and continuous learning
• Competitive salary and company benefits
• Work-from-home arrangement (the arrangement may vary depending on the work nature of the business team)