Data Scientist, AML Models and Analytics - Client Risk Rating

About this role

Requisition ID: 253787

Join a purpose driven winning team, committed to results, in an inclusive and high-performing culture.

The Data Scientist contributes to the overall success of the Global Compliance and AML team by analyzing and improving CRR models, thereby enhancing operating efficiency, and ensuring all work is conducted in compliance with governing regulations, internal policies, and procedures. This role will be responsible for designing, enhancing, and implementing data-driven solutions supporting the Bank's AML Client Risk Rating (CRR) models.

The ideal candidate will have a strong background in data analysis, statistical modelling, and machine learning.

Is this role right for you? In this role, you will:

• Apply a range of modeling techniques, from basic statistical models to AI and Machine Learning, to develop, maintain, and implement models for Client Risk Rating case generation in support of Scotiabank’s AML program
• Create and maintain documentation for models and results data analysis
• Collaborate and coordinate with teams from model validation, internal audit, and external audit to ensure that models meet all legal and regulatory requirements
• Communicate complex data findings and insights to technical and non-technical stakeholders
• Support communication with first and second lines of business and other stakeholders, and recommend changes to AML Client Risk Rating models as appropriate
• Support communication with model users to ensure that model limitations are properly understood and that models meet model governance and usage requirements

Do you have the skills that will enable you to succeed? We’d love to work with you if you have experience with:

• Bachelor’s degree or higher within STEAM (Science, Technology, Engineering, Analytics, or Mathematics), statistics, data science, artificial intelligence, computer science, computer engineering, financial modelling, or a related field
• Experience with statistical m...