Lead financial crime analytics for RBC’s Global AML program in Toronto, combining model performance analysis with Python, SQL, and data science.

As a Lead Data Scientist, you will translate project needs into detection models and provide relevant intelligence for transaction monitoring. Your work includes model documentation, monitoring methodology, quantitative analysis, and performance recommendations under Enterprise Model Risk Management Standards. Financial Crime or Transaction Monitoring Analytics experience is valued, alongside supervised or unsupervised learning and NLP experience.

Key Responsibilities:

- Develop validation ready model documentation and monitoring methodology
- Create performance metrics and communicate findings to leadership
- Collaborate across Monitoring and Analytics teams




- Apply recent technologies to AML analytics opportunities
- Identify industry best practices for strategic consideration

Requirements:

- Five plus years conducting in depth data analytics
- Analytical university or master's degree
- Python, Scala or Spark, SQL, Hive, and GitHub familiarity
- Strong attention to documentation, testing artifacts, and quantitative analysis
- Understanding of AML and financial crime fundamentals

Bring model risk analytics, financial crime understanding, and clear data storytelling to RBC’s Toronto-based AML monitoring work.

📌 Financial Crime Analytics Lead Scientist (Vancouver)
🏢 RBC
📍 Vancouver