Lead AML data science at RBC in Toronto, applying Python and Spark to transaction monitoring models. This full time role analyzes elevated client risk.

As Lead Data Scientist, Global AML Transaction Monitoring, you will develop and maintain machine learning and rule based solutions. The work combines large dataset analysis, exploratory data analysis, feature development, and AML fundamentals. You will create intelligence for the Global AML program while considering regulatory expectations, industry trends, and innovation opportunities.

Key Responsibilities:

- Develop, tune, and maintain AML transaction monitoring solutions
- Analyze data to identify elevated financial and reputational risk
- Create validation ready documentation and monitoring methodologies




- Document performance metrics and recommend model improvements
- Identify current technologies and industry practices for AML analytics

Requirements:

- Five plus years conducting in depth data analytics
- University or master's degree in an analytical field
- Expertise with Python and Scala or Spark
- Proficiency with SQL, Hive databases, and GitHub
- Understanding of AML and financial crime fundamentals

Use data science, model analytics, and transaction monitoring expertise to strengthen RBC’s Global AML program in Toronto.

📌 Lead AML Data Scientist Toronto (Vancouver)
🏢 RBC
📍 Vancouver