As a Senior Data Scientist in our Compliance Technology & Data Strategy function, you will play a key role in shaping our approach to governance, independent validation, and continuous improvement of our financial crime compliance (FCC) systems and data platforms. You will lead the design, testing, validation, and monitoring of advanced models and data pipelines that support our transaction monitoring (TM), customer risk rating (CRR), and watchlist screening engines across both fiat and crypto ecosystems.
You will work hands-on with large, complex datasets, building and validating rule-based and ML-enhanced models, developing new typology detection logic, and ensuring our systems remain robust and defensible in an evolving regulatory landscape. This is a highly technical, cross-functional role requiring strong programming, data engineering, and advanced analytics skills, along with an understanding of FCC domain risks.
You’ll partner closely with Compliance, Product, Engineering, and Risk teams to modernize our compliance technology stack, drive improvements in data pipelines and model explainability, and integrate cutting-edge blockchain intelligence and cross-chain analytics tools. Your work will directly impact our ability to detect and mitigate financial crime risks, meet regulatory expectations, and deliver trusted insights across our global operations.
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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
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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
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Develop and execute robust testing strategies to assess model fitness, typology coverage, Type I and Type II error rates, and regulatory defensibility
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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
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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
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Build reproducible and production-ready notebooks, scripts, and workflows following best practices in version control, code testing, and documentation
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Leverage advanced anomaly detection techniques, clustering, and graph analytics to identify emerging financial crime typologies across large multi-source datasets
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Conduct deep-dive data investigations, develop new detection typologies, and translate FCC risk scenarios into effective, explainable models and analytics
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Partner with Product and Engineering to drive enhancements to compliance data architecture, including streaming pipelines, data integrations, and advanced analytics capabilities
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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
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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)
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Contribute to developing reusable data science tools, libraries, or model templates that improve the speed and consistency of future compliance analytics work
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Stay current on regulatory expectations and industry best practices for model governance, validation, and development (NYDFS, FATF, HKMA, MAS, FCA, etc.)
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Produce clear, actionable reports and data visualizations to communicate findings to technical and non-technical stakeholders
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Proactively research and experiment with new open-source tools and techniques that could enhance our FCC model governance and analytics capability
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7+ years of hands-on experience in data science, machine learning, or advanced analytics, ideally in the FCC, AML, KYC, or fraud detection domain
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Proven experience building and validating data models using programming languages such as Python, SQL, Java, R, or similar
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Strong skills in data engineering, large-scale data pipelines, ETL/ELT processes, and streaming analytics (Spark, Kafka, Snowflake, or equivalent)
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Familiarity with blockchain analytics tools (e.g., Chainalysis, TRM Labs, Elliptic) and understanding of on-chain transaction monitoring is highly desirable
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Experience with building, tuning, and monitoring rule-based and ML models for financial crime detection, risk scoring, or sanctions screening
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Solid background in building and maintaining scalable data pipelines, streaming analytics, and working with large, complex datasets
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Experience deploying models into production environments, working with containerization (e.g., Docker, Kubernetes) and cloud data tools
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Solid understanding of typology detection, false positive/negative tuning, and regulatory model validation expectations
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Strong background in data visualization and reporting using BI tools (Tableau, Looker, Power BI, or similar)
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Excellent communication skills to present complex technical findings and recommendations to diverse stakeholders
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Proven ability to work independently in a fast-paced, cross-functional environment
