Manager, Client Risk Rating AML

What is the opportunity?

The Manager, Client Risk Rating (AML) is responsible for developing and enhancing data-driven models that assess client risk scores as part of the Anti-Money Laundering (AML) program. This role focuses on data analysis, feature engineering, and machine learning techniques to improve the accuracy and efficiency of client risk classification while ensuring regulatory compliance.

By working with structured financial data and risk indicators, the Data Scientist will support the design, implementation, and monitoring of risk assessment models that help detect and prevent financial crimes.

What will you do?

• Support the development of client risk rating models by applying statistical methods and machine learning techniques to analyze customer risk factors.
• Perform data extraction, cleansing, and transformation using SQL and Python to prepare datasets for modeling.
• Implement basic machine learning models (e.g., logistic regression, decision trees, random forests) to improve risk classification.
• Monitor model performance and assist in fine-tuning models by adjusting risk factors and thresholds.
• Work with large-scale structured datasets (e.g., customer profiles, transaction histories) to identify key risk indicators.
• Assist in feature engineering by integrating client attributes, geographic risks, and transaction behaviors into risk models.
• Collaborate with compliance and regulatory teams to ensure models align with AML/KYC policies and reporting requirements.
• Create reports and dashboards to visualize risk trends and model results for business stakeholders.
• Document model assumptions, limitations, and validation processes to support regulatory audits and governance requirements.

What do you need to succeed?

Must-have

• 2+ years of experience in data analysis, machine learning, or risk modeling in a financial or compliance-related environment.
• Proficiency in Python, R, or SQL for data analysis and model building.
• Experience with basic machine learning models (e.g., logistic regression, decision trees) and understanding of model evaluation techniques.
• Ability to work with large datasets and apply feature engineering for predictive modeling.
• Strong data visualization skills (e.g., Tableau, Power BI, Matplotlib, or Seaborn) to present findings effectively.
• Good communication skills to collaborate with compliance teams and explain analytical findings.

Nice-to-have

• Experience in AML, financial crime compliance, or risk management.
• Familiarity with regulatory requirements related to KYC/AML risk assessment.
• Exposure to cloud-based ML tools (AWS, Azure, or GCP).
• Certification such as Certified Anti-Money Laundering Specialist (CAMS).

What's in it for you?

We thrive on the challenge to be our best, progressive thinking to keep growing, and working together to deliver trusted advice to help our clients thrive and communities prosper. We care about each other, reaching our potential, making a difference to our communities, and achieving success that is mutual.

• A comprehensive Total Rewards Program including bonuses and flexible benefits, competitive compensation
• Leaders who support your development through coaching and managing opportunities
• Work in a dynamic, collaborative, progressive, and high-performing team
• Opportunities to do challenging work
• Flexible work/life balance options

Job Skills

Additional Job Details

Note: Applications will be accepted until 11:59 PM on the day prior to the application deadline date above

Inclusion and Equal Opportunity Employment

At RBC, we believe an inclusive workplace that has diverse perspectives is core to our continued growth as one of the largest and most successful banks in the world. Maintaining a workplace where our employees feel supported to perform at their best, effectively collaborate, drive innovation, and grow professionally helps to bring our Purpose to life and create value for our clients and communities. RBC strives to deliver this through policies and programs intended to foster a workplace based on respect, belonging and opportunity for all.