Data Scientist, Reinforcement Learning

About the Role

Responsibilities:

• Entity Clustering Algorithm Development
• 1. Design address clustering algorithms for UTXO chains (Bitcoin, Litecoin, BCH) with near 100% accuracy
• 2. Develop Common-Spend clustering, EVM clustering, and multi-layer blockchain unification
• 3. Build cross-chain entity linking systems to track fund flows across blockchains
• Machine Learning Model Development
• 1. Build and optimize risk scoring models to assess address and entity risk levels
• 2. Develop address evidence quality models with full audit trails
• 3. Create behavioral attribution models to classify unknown addresses
• 4. Implement change address prediction and anomaly detection algorithms
• Behavioral Pattern Recognition
• 1. Design heuristic algorithms to identify legitimate services (exchanges, wallets, DeFi)
• 2. Develop detection systems for illicit activities (mixers, scams, fraud contracts)
• 3. Build hot/cold wallet distinction and address rotation pattern recognition
• Algorithm Quality Assurance
• 1. Monitor algorithm performance and maintain <1% false positive rate
• 2. Conduct root cause analysis and implement improvements
• 3. Design A/B testing frameworks and establish performance benchmarks
• 4. Ensure algorithm explainability and auditability
• Technical Innovation & Research
• 1. Adapt algorithms for new blockchains and protocol updates
• 2. Research graph neural networks for risk propagation
• 3. Publish technical papers and contribute to blockchain analytics community

Requirements:

• Master's or Ph.D. in Computer Science, Mathematics, Statistics, or related fields
• 5+ years of algorithm development, machine learning, or data science experience
• 3+ years of blockchain technology and cryptocurrency analytics experience (preferred)
• Track record of deploying algorithms in production environments
• Programming: Expert in Python; proficient in Scala/Java for big data
• SQL & DB: Advanced SQL (Hive/Spark/Trino); graph databases (Neo4j)
• Machine Learning: Strong ML foundation; scikit-learn, XGBoost, PyTorch
• Graph Algorithms: Graph theory, community detection, PageRank; GNN preferred
• Big Data: Spark, distributed computing, stream processing
• Blockchain: UTXO vs Account models, smart contracts, cross-chain bridges
• Mathematics: Statistics, probability, linear algebra, optimization

Preferred Qualifications

• Published research in blockchain analytics, graph algorithms, or ML
• Contributions to open-source blockchain analytics projects
• Graph Neural Networks (GNN) or deep learning on graphs experience
• Knowledge of financial crime patterns and AML/KYC compliance
• Familiarity with privacy-enhancing technologies

Why Binance