Director, Fraud Data and Analytics

Scotiabank•Toronto, ON

1d•Hybrid

About The Position

The Global Fraud Technology team develops and manages enterprise fraud capabilities that protect Scotiabank, its customers, and its employees across all channels and products. The Fraud Data & Analytics organization is responsible for the data, analytics, intelligence, and AI foundations that power fraud detection, fraud response, scam prevention, investigations, and risk management capabilities across the Bank. We partner closely with Fraud Operations, Fraud Strategy, Data Science, Risk Management, AML, Payments, Digital Banking, Enterprise Data Office, and Enterprise Technology teams to deliver trusted, scalable, and governed fraud data capabilities that support real-time decisioning, advanced analytics, machine learning, regulatory reporting, and operational insights. The Director, Global Fraud Technology – Fraud Data & Analytics is accountable for the strategy, delivery, governance, and operation of the Bank’s fraud data and analytics ecosystem. This role leads teams responsible for fraud data platforms, feature engineering, data products, fraud intelligence capabilities, reporting and visualization platforms, model enablement services, and AI-driven analytical solutions. The Director is responsible for ensuring fraud data is trusted, accessible, timely, secure, and governed while enabling advanced analytical capabilities that improve fraud detection effectiveness, customer protection, operational efficiency, and business decision-making. The role works closely with business and technology leaders to define the future-state fraud data strategy and drive enterprise-wide adoption of modern data and AI capabilities.

Requirements

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.

Nice To Haves

Experience supporting fraud detection, fraud response, financial crime, AML, or cyber analytics functions.
Experience with graph databases, network analytics, fraud intelligence platforms, and behavioral 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.

Responsibilities

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 realtime 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 behavioral 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, behavioral 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.
Champions a customer-focused culture to deepen client relationships and leverage broader Bank relationships, systems, and knowledge.
Understands how the Bank’s risk appetite and risk culture should be considered in day-to-day activities and decisions.
Actively pursues 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.