Director, Fraud Data & Analytics - Scotiabank, Toronto

Position Summary

Join Scotiabank's Global Fraud Technology team as a Director, Fraud Data & Analytics, a pivotal role focused on safeguarding the Bank, its customers, and employees. This team is responsible for developing and managing enterprise fraud capabilities, leveraging cutting-edge technology to protect against evolving threats. The Fraud Data & Analytics organization specifically builds the data, analytics, intelligence, and AI foundations that power critical fraud detection, response, scam prevention, investigations, and risk management capabilities across the entire Bank.

As the Director, Global Fraud Technology – Fraud Data & Analytics, you will be accountable for the comprehensive strategy, delivery, governance, and operation of Scotiabank’s fraud data and analytical ecosystem. This leadership position involves guiding teams responsible for fraud data platforms, feature engineering, data products, fraud intelligence capabilities, reporting, visualization platforms, model enablement services, and advanced AI-driven analytical solutions. Your core mission will be to ensure fraud data is trusted, accessible, timely, secure, and well-governed, thereby enabling sophisticated analytical capabilities that enhance fraud detection effectiveness, bolster customer protection, improve operational efficiency, and inform critical business decision-making. You will collaborate closely with business and technology leaders to shape the future-state fraud data strategy and drive enterprise-wide adoption of modern data and AI capabilities.

Key Duties

• Develop and execute the multi-year fraud data and analytics strategy aligned with enterprise fraud, risk, and technology objectives.
• Define the target-state fraud data architecture and operating model supporting real-time and batch analytical workloads.
• Establish strategic roadmaps for fraud data platforms, data products, AI enablement capabilities, and analytical services.
• Partner with senior business and technology stakeholders to prioritize investments and define long-term analytical capabilities.
• Drive innovation through the adoption of modern data platforms, advanced analytics, AI, and machine learning technologies.
• Lead strategic vendor and technology partner relationships supporting fraud data and analytics capabilities.
• Own the fraud data ecosystem supporting fraud detection, fraud response, investigations, scam prevention, and fraud intelligence functions.
• Establish scalable and resilient data platforms supporting high-volume transactional and behavioural data processing.
• Drive modernization initiatives involving cloud-native data architectures, streaming platforms, data lakes, and analytical environments.
• Ensure seamless integration of internal and external fraud data sources across the enterprise.
• Deliver high-quality, trusted, and governed fraud data assets for operational and analytical consumption.
• Lead development and management of enterprise fraud data products and reusable analytical assets.
• Establish feature engineering capabilities supporting fraud detection models, AI solutions, and advanced analytics initiatives.
• Build and maintain fraud-specific feature stores and analytical datasets.
• Drive standardization, reuse, and scalability across fraud data assets.
• Partner with business stakeholders to define and prioritize strategic data products.
• Deliver enterprise fraud intelligence capabilities that provide actionable insights into fraud trends, emerging threats, scam typologies, and customer risk.
• Enable advanced analytical capabilities including network analytics, graph intelligence, behavioural profiling, anomaly detection, and predictive analytics.
• Partner with Fraud Strategy and Fraud Operations teams to identify opportunities for fraud loss reduction and operational optimization.
• Establish enterprise reporting and visualization capabilities supporting executive, operational, and regulatory reporting needs.
• Drive analytical innovation through the application of AI and emerging technologies.
• Provide technology platforms and services supporting fraud data science and machine learning teams.
• Enable the full model lifecycle including development, deployment, monitoring, explainability, performance measurement, and governance.
• Support deployment of AI-driven fraud capabilities across detection, response, and intelligence functions.
• Establish MLOps and analytical operations capabilities that improve model reliability and scalability.
• Partner with Model Risk Management and Validation teams to support governance requirements.
• Establish and maintain data governance frameworks supporting fraud data assets.
• Ensure compliance with regulatory requirements, privacy obligations, data retention standards, and information security policies.
• Define and monitor data quality standards, controls, lineage, and stewardship practices.
• Partner with Enterprise Data Office and Risk Management teams to strengthen fraud data governance and accountability.
• Ensure appropriate controls exist around analytical models, reporting, and data usage.
• Establish performance metrics, service-level objectives, and operational controls across fraud data platforms.
• Drive continuous improvements in data quality, availability, timeliness, and operational efficiency.
• Ensure platform resiliency, disaster recovery readiness, and operational support processes meet enterprise standards.
• Manage portfolio budgets, vendor relationships, and strategic investments.
• Deliver measurable business outcomes through improved data accessibility, analytical capabilities, and operational effectiveness.
• Build, lead, and develop high-performing teams across data engineering, analytics engineering, platform engineering, data management, and analytical enablement functions.
• Coach and mentor senior managers, architects, and technical leaders.
• Foster a culture of innovation, experimentation, continuous learning, and operational excellence.
• Champion Agile delivery methodologies, DataOps, MLOps, and product-oriented operating models.
• Develop succession plans and talent strategies for critical leadership and technical roles.
• Champion a customer-focused culture to deepen client relationships and leverage broader Bank relationships, systems, and knowledge.
• Understand how the Bank’s risk appetite and risk culture should be considered in day-to-day activities and decisions.
• Actively pursue effective and efficient operations in accordance with Scotiabank’s Values, Code of Conduct, and Global Sales Principles while ensuring the adequacy, adherence to, and effectiveness of business controls relating to operational, compliance, AML/ATF/sanctions, conduct, model, and technology risk.

What You'll Bring

To succeed in this challenging Director, Fraud Data & Analytics role at Scotiabank, you will need a robust background in technology leadership, particularly within data and analytics, alongside a deep understanding of financial services environments.

• 10+ years of progressive technology leadership experience in data, analytics, AI, or enterprise platform organizations.
• 5+ years of experience leading large-scale data and analytics platforms within financial services, fraud, risk, AML, or related domains.
• 5+ years of leadership experience managing managers and multidisciplinary technical teams.
• Deep expertise in modern data architectures, data engineering, streaming technologies, cloud data platforms, and analytical ecosystems.
• Experience supporting machine learning, AI, and advanced analytics capabilities in production environments.
• Strong understanding of fraud analytics, fraud intelligence, feature engineering, and model enablement concepts.
• Experience implementing enterprise data governance, data quality, lineage, and stewardship programs.
• Strong knowledge of cloud platforms, preferably Google Cloud Platform (GCP) and Azure.
• Experience managing strategic roadmaps, budgets, vendor relationships, and transformation initiatives.
• Strong executive communication and stakeholder management skills.
• Experience operating within highly regulated financial services environments.

While not strictly required, the following experiences would be considered a significant asset:

• Experience supporting fraud detection, fraud response, financial crime, AML, or cyber analytics functions.
• Experience with graph databases, network analytics, fraud intelligence platforms, and behavioural analytics solutions.
• Knowledge of MLOps, AI governance, model risk management, and analytical operations frameworks.
• Experience with real-time streaming technologies and event-driven analytical architectures.
• Understanding regulatory reporting, privacy requirements, and enterprise data governance frameworks.
• Experience leading global teams across multiple geographies.

About the Employer

As Canada's International Bank, Scotiabank is a truly diverse and global team, speaking over 100 languages with backgrounds from more than 120 countries. Scotiabank values the unique skills and experiences each individual brings to the Bank and is deeply committed to creating and maintaining an inclusive and accessible environment for everyone. They offer accommodation during the recruitment and selection process for candidates who require it.

The workplace at Scotiabank fosters an inclusive and collaborative environment that actively encourages creativity, curiosity, and celebrates success. You will have the opportunity to work with and learn from diverse industry leaders who have joined from top technology companies worldwide. Scotiabank prioritizes innovation and continuous learning, caring about its people by allowing them to design how they work to deliver exceptional results. Employees are offered a competitive total rewards package, including a performance bonus, company matching programs (on pension & profit sharing), and generous vacation.