Lieu de travail : Toronto, Ontario, Canada Horaire : 37.5 Secteur d’activité : Analyses, informations et intelligence artificielle Détails de la rémunération : $96,900 - $136,800 CAD La TD a à cœur d’offrir une rémunération juste et équitable à tous les collègues. Les occasions de croissance et le perfectionnement des compétences sont des caractéristiques essentielles de l’expérience collègue à la TD. Nos politiques et pratiques en matière de rémunération ont été conçues pour permettre aux collègues de progresser dans l’échelle salariale au fil du temps, à mesure qu’ils s’améliorent dans leurs fonctions. Le salaire de base offert peut varier en fonction des compétences et de son expérience du candidat, de ses connaissances professionnelles, de son emplacement géographique et d’autres besoins particuliers du secteur et de l’entreprise. En tant que candidat, nous vous encourageons à poser des questions sur la rémunération et à avoir une conversation franche avec votre recruteur, qui pourra vous fournir des détails plus précis sur ce poste. Description du poste : R_ Manager, Analytics and Insights (Financial Crimes Network Analytics) Please note this is an Individual Contributor role with no People Management asccountabilities at this time. It is in-office 3 days per week. Summary Join the Advanced Analytics & Insights team within Financial Crimes Risk Management (FCRM) Canada to advance an innovative Financial Crimes Network Threat Detection & Prioritization capability. This high-impact initiative focuses on uncovering hidden high-risk customer connections that conventional detection methods often miss,



enabling effective risk identification, prioritization, and strategic insights. The Analytics & Insights Manager will lead the enhancement & operationalization of this detection capability, primarily including developing and accessing analytical detection methods, generating risk insights and actionable recommendations. Key Accountabilities Financial Crime Analytics & Detection Translate financial crime typologies into detection strategies, generate hypothesis, design analytical approaches, develop querying logic, and validate findings through data analysis. Perform periodic Exploratory Data Analysis to identify emerging risk patterns, customer characteristics, and risk operation insights. Synthesize and present data-driven findings and recommendations to technical and non-technical audiences. Insights & Decision Support Assess coverage and performance of existing models/analytical solutions, and identify potential gaps or opportunities for enhancement. Develop compelling visualizations and presentations that communicate analytical findings in an intuitive and decision-oriented manner.



Ad-hoc Analysis Translate business problems into structured analytical problems and develop practical solutions Conduct ad-hoc analysis including such as root-cause analysis and impact analysis to meet business objectives Perform independent analytical review and effective challenge of existing methodologies and analytical solutions; assess assumptions, data, results, and business implications and provide constructive recommendations for enhancement. Contribute to documentation, governance, controls, and ongoing monitoring required Stakeholder & Process Management Serve as an analytical partner to financial crime subject-matter experts, investigators, EDD teams, data scientists, technology teams, and other stakeholders. Bridge financial crime business requirements and analytical execution by translating domain problems into explicit analytical questions and requirements. Develop process to coordinate various stakeholders for recurring activities. Contribute to documentation, governance, controls, and ongoing monitoring required Required Qualifications & Skills Core skills: SQL, Python for data analytics; statistical thinking and problem solving; data-driven story telling skills, visualizations tool Tableau/Power BI. Mathematical / statistical modelling experience, AI agentic experience are bonus. Domain knowledge: Anti-financial crime (money laundering, terrorist financing), regulatory and governance knowledge and working experience are beneficial but not required; training and guidance will be provided. Education: Bachelor's degree in STEM (e.G., Science, Technology, Engineering, Mathematics, Statistics, Data Science, Economics). Work Experience: 2-3 years or above, intern / co-op experience in analytical fields is counted. Why join us Anti-Money Laundering (AML) is TD

📌 Manager, Analytics And Insights (Financial Crimes Network Analytics) (Toronto)
🏢 TD Bank Group
📍 Toronto