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Data Scientist, AML Analytics - RBC
Data Scientist
Lead the design and enhancement of AML/ATF transaction monitoring systems, leveraging Python, SQL, and machine learning to detect suspicious activity and elevate client risk profiles for a global financial institution.
About the role
Key Responsibilities
- Develop and refine AML/ATF transaction monitoring models using Python and SQL, ensuring alignment with regulatory expectations and industry best practices.
- Analyze large datasets to identify patterns of suspicious activity and elevate client risk profiles, providing actionable intelligence to the Global AML program.
- Collaborate with cross‑functional teams to integrate new data sources, innovate analytical techniques, and continuously improve detection accuracy.
- Document model logic, performance metrics, and validation results, maintaining rigorous audit trails for regulatory compliance.
- Stay current on emerging AML trends, regulatory changes, and technological advancements to proactively enhance monitoring capabilities.
Requirements
- Proven experience in data science or analytics within AML/ATF or related financial crime domain.
- Strong programming skills in Python and proficiency with SQL for data extraction and manipulation.
- Hands‑on experience building and deploying machine learning models for fraud detection or risk scoring.
- Excellent analytical, problem‑solving, and communication skills, with the ability to translate complex findings into clear business insights.
- Knowledge of regulatory frameworks (e.g., FATF, OFAC) and familiarity with transaction monitoring platforms is a plus.
Skills
pythonsqlmachine learningdata analysis