OKX is looking to shape its approach to governance, independent validation, and continuous improvement of its financial crime compliance (FCC) systems and data platforms by hiring a Senior Data Scientist.
Requirements
Proven experience building and validating data models using programming languages such as Python, SQL, Java, R, or similar
Strong skills in data engineering, large-scale data pipelines, ETL/ELT processes, and streaming analytics (Spark, Kafka, Snowflake, or equivalent)
Familiarity with blockchain analytics tools (e.g., Chainalysis, TRM Labs, Elliptic) and understanding of on-chain transaction monitoring is highly desirable
Experience with building, tuning, and monitoring rule-based and ML models for financial crime detection, risk scoring, or sanctions screening
Solid background in building and maintaining scalable data pipelines, streaming analytics, and working with large, complex datasets
Experience deploying models into production environments, working with containerization (e.g., Docker, Kubernetes) and cloud data tools
Solid understanding of typology detection, false positive/negative tuning, and regulatory model validation expectations
Responsibilities
Design, test, and independently validate rule-based and machine learning models for transaction monitoring, customer risk scoring, sanctions and watchlist screening, and typology detection for both fiat and crypto transactions
Build and optimize scalable data pipelines integrating blockchain analytics, on-chain and off-chain transaction data, and third-party intelligence tools to enhance risk detection
Develop and execute robust testing strategies to assess model fitness, typology coverage, Type I and Type II error rates, and regulatory defensibility
Design strategies to automate monitoring frameworks for model performance, data quality, and risk typology drift; implement advanced analytics to detect anomalies and continuously tune models
Lead the development and execution of comprehensive metrics and data reporting frameworks, ensuring the accuracy, consistency, and timeliness of key risk indicators, model performance metrics, and regulatory reporting requirements across all FCC models
Build reproducible and production-ready notebooks, scripts, and workflows following best practices in version control, code testing, and documentation
Leverage advanced anomaly detection techniques, clustering, and graph analytics to identify emerging financial crime typologies across large multi-source datasets
Other
7+ years of hands-on experience in data science, machine learning, or advanced analytics, ideally in the FCC, AML, KYC, or fraud detection domain
Excellent communication skills to present complex technical findings and recommendations to diverse stakeholders
Proven ability to work independently in a fast-paced, cross-functional environment
Stay current on regulatory expectations and industry best practices for model governance, validation, and development (NYDFS, FATF, HKMA, MAS, FCA, etc.)
Produce clear, actionable reports and data visualizations to communicate findings to technical and non-technical stakeholders
