Build suspicious-activity detection at RBC through AML transaction monitoring, machine learning, and rule based solutions. Work full time in Toronto.

This Lead Data Scientist role develops current monitoring solutions and tunes existing models used across the Global AML program. You will analyze large datasets with Python and Scala or Spark, apply exploratory analysis and feature development, and assess client financial and reputational risk. Collaboration with directors, monitoring teams, business partners, and internal stakeholders supports effective risk remediation.

Key Responsibilities:
Develop current AML monitoring solutions using data science techniques
Tune machine learning and rule based transaction monitoring models
Collaborate to prioritize monitoring solution development




Remediate RBC risk exposure through monitoring implementation
Improve program effectiveness and efficiency with stakeholders

Requirements:
Five plus years of in depth data analytics experience
Degree in computer science, engineering, mathematics, statistics, or related field
Large dataset analysis and manipulation expertise
Robust exploratory data analysis and feature development capabilities
Ability to explain findings to technical and non technical stakeholders

Apply AML knowledge, Python, Spark, and analytical communication skills to develop stronger suspicious-activity detection models at RBC.
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📌 Aml Transaction Monitoring Scientist Vancouver
🏢 RBC
📍 Vancouver