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Director, Fraud Data and Analytics

Scotiabank
Toronto, CAN
Full Time
Director
3 weeks ago
Data ArchitectureData EngineeringCloud Platforms (GCP, Azure)Machine LearningFraud AnalyticsData Governance
Free

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Data ArchitectureData EngineeringCloud Platforms (GCP, Azure)
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The Team

  • 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.

The Role

  • 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.

Key Accountabilities Strategy & Leadership

  • 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.

Fraud Data Platform Ownership

  • 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.

Data Products & Feature Engineering

  • 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.

Analytics & Fraud Intelligence

  • 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.

AI & Model Enablement

  • 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.

Data Governance & Risk Management

  • 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.

Operational Excellence

  • 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.

Talent Leadership

  • 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.

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