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OKX · 10 hours ago

Senior Data Scientist, Compliance Technology

San Jose, CA

Full-time

Onsite

Senior Level

$143K/yr - $214K/yr

7+ years exp

OKX is a leading crypto exchange that is reshaping the future with crypto. The Senior Data Scientist will play a key role in enhancing the company's financial crime compliance systems and data platforms, focusing on transaction monitoring and customer risk assessment.

AppsBitcoinBlockchainCryptocurrencyFinanceFinancial ServicesFinTechInformation TechnologyInternetWeb3

Comp. & Benefits

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

Conduct deep-dive data investigations, develop new detection typologies, and translate FCC risk scenarios into effective, explainable models and analytics

Partner with Product and Engineering to drive enhancements to compliance data architecture, including streaming pipelines, data integrations, and advanced analytics capabilities

Lead remediation of data quality and model coverage gaps by executing data audits and root cause analysis; maintain thorough documentation and audit trails for regulatory readiness

Deploy containerized data science workflows and collaborate with engineering teams to integrate models seamlessly into production environments (e.g., using Docker, Kubernetes, or cloud pipelines)

Contribute to developing reusable data science tools, libraries, or model templates that improve the speed and consistency of future compliance analytics work

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

Proactively research and experiment with new open-source tools and techniques that could enhance our FCC model governance and analytics capability

Qualification

Data ScienceMachine LearningData EngineeringFinancial Crime CompliancePythonSQLETL/ELT ProcessesBlockchain AnalyticsData VisualizationCommunicationCross-Functional Collaboration

Required

7+ years of hands-on experience in data science, machine learning, or advanced analytics, ideally in the FCC, AML, KYC, or fraud detection domain

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)

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

Strong background in data visualization and reporting using BI tools (Tableau, Looker, Power BI, or similar)

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

Preferred

Familiarity with blockchain analytics tools (e.g., Chainalysis, TRM Labs, Elliptic) and understanding of on-chain transaction monitoring is highly desirable