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Toronto, ON, Canada
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Key Responsibilities
Advanced Analytics & AI Leadership
• Design, develop, and deploy advanced analytical, machine learning, and AI models for Wealth Management use cases.
• Lead data science initiatives across areas such as:
o Portfolio construction & optimization
o Client segmentation & personalization
o Predictive analytics and behavioral insights
o Risk, suitability, and performance analytics
• Translate complex analytical outputs into clear business insights for senior stakeholders.
Wealth Management Enablement
• Partner with Wealth Management business, product, and advisory teams to frame high value analytics and AI use cases.
• Apply domain understanding across investment products, advisory workflows, and client lifecycle management.
• Support data driven decision making while addressing explainability, transparency, and trust requirements critical to Wealth platforms.
Data, Modeling & Experimentation
• Guide feature engineering, model selection, validation, and performance evaluation.
• Lead experimentation, hypothesis testing, and continuous model improvement.
• Ensure data quality, robustness, and scalability of analytical solutions.
Production & Governance
• Collaborate with Engineering, MLOps, and Platform teams to operationalize models reliably.
• Ensure alignment with regulatory, risk, data privacy, and responsible AI standards.
• Support model documentation, explainability, and audit readiness.
Team & Stakeholder Leadership
• Mentor junior data scientists and analysts, driving best practices and analytical rigor.
• Act as a trusted advisor to business, technology, and leadership stakeholders.
• Contribute to strategic analytics roadmaps and enterprise AI initiatives.
Required Skills & Experience
• 8–12+ years of experience in data science, advanced analytics, or quantitative roles.
• Strong experience applying ML/statistical models in large scale enterprise environments.
• Proven Wealth Management or Investment domain experience, including portfolios, advisory, or client analytics.
• Proficiency in Python/R and data science tools; strong statistical and analytical foundations.
• Experience working with large datasets, data platforms, and modern analytics pipelines
Bachelor's degree
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