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Director Machine Learning information

What is a director machine learning?

A Director of Machine Learning leads teams in developing and deploying machine learning models to solve business challenges. They define the AI strategy, oversee research, and ensure models are scalable and ethical. This role requires expertise in machine learning, data science, and leadership, as well as collaboration with cross-functional teams. Directors also stay updated on industry advancements and drive innovation within their organizations.

What are the primary responsibilities and challenges faced by a director machine learning on a daily basis?

A Director of Machine Learning is typically responsible for overseeing the development and deployment of machine learning solutions, mentoring technical teams, setting strategic direction for AI initiatives, and ensuring the alignment of projects with organizational goals. Challenges often include balancing innovative research with business priorities, navigating evolving technology landscapes, and coordinating efforts across data science, engineering, and stakeholder teams. This role requires regular collaboration with product managers, executives, and cross-functional departments to prioritize initiatives and communicate complex technical concepts. Successful directors excel at fostering a culture of continuous learning, optimizing team productivity, and staying ahead in a fast-paced, rapidly changing field.

What are the key skills and qualifications needed to thrive in the director machine learning position, and why are they important?

To thrive as a Director Machine Learning, you need advanced expertise in machine learning, statistics, data science, and leadership, typically supported by a master's or Ph.D. in a related field and several years of relevant industry experience. Familiarity with tools such as Python, TensorFlow or PyTorch, cloud platforms, and data management systems, as well as certifications like AWS Certified Machine Learning or Google Professional Machine Learning Engineer, are commonly required. Exceptional communication, strategic thinking, and team management skills distinguish top candidates in this role. These capabilities are essential for driving organizational AI initiatives, fostering high-performing teams, and delivering impactful business solutions.

Is a machine learning director a high paying job?

A machine learning director typically earns a high salary due to the specialized skills, leadership responsibilities, and experience required for the role. Compensation often includes base salary, bonuses, and stock options, reflecting the demand for expertise in AI and data science. Salaries can vary based on industry, company size, and location, but generally rank among the higher-paying technology leadership positions.

What does a director of machine learning do?

A director of machine learning oversees the development and implementation of machine learning strategies and projects within an organization. They lead teams of data scientists and engineers, set technical goals, ensure project alignment with business objectives, and often collaborate with other departments to integrate AI solutions using tools like Python, TensorFlow, or PyTorch.

What are the most commonly searched types of Machine Learning jobs in Ontario?

The most popular types of Machine Learning jobs in Ontario are:

What are popular job titles related to Director Machine Learning jobs in Ontario?

For Director Machine Learning jobs in Ontario, the most frequently searched job titles are:

What job categories do people searching Director Machine Learning jobs in Ontario look for?

The top searched job categories for Director Machine Learning jobs in Ontario are:

Director Machine Learning jobs near you
Infographic showing various Director Machine Learning job openings in Ontario as of August 2026, with employment types broken down into 1% As Needed, 70% Full Time, 28% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Director, Fraud Data and Analytics

Scotiabank

Toronto, ON • On-site

Full-time

Retirement, PTO

Re-posted 11 days ago

Job description

Requisition ID: 264919 
Join a purpose driven winning team, committed to results, in an inclusive and high-performing culture.

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.

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 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.

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.

  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 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. 

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.

General
  • 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.
  What You Will Bring to Succeed Must Have
  • 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 Have
  • 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.

The Workplace

  • We are technology partners who help the business transform how our employees around the world work.  
  • We have an inclusive and collaborative working environment that encourages creativity, curiosity, and celebrates success!  
  • You'll get to work with and learn from diverse industry leaders, who have hailed from top technology companies around the world.  
  • We foster an environment of innovation and continuous learning.  
  • We care about our people, allowing them to design how they work to deliver amazing results.  
  • We offer a competitive total rewards package, including a performance bonus, company matching programs (on pension & profit sharing), and generous vacation.  
Scotiabank  

As Canada's International Bank, we are a diverse and global team. We speak more than 100 languages with backgrounds from more than 120 countries. We value the unique skills and experiences each individual brings to the Bank and are committed to creating and maintaining an inclusive and accessible environment for everyone. If you require accommodation (including, but not limited to, an accessible interview site, alternate format documents, ASL Interpreter, or Assistive Technology) during the recruitment and selection process, please let our Recruitment team know. If you require technical assistance, please click here. Candidates must apply directly online to be considered for this role. We thank all applicants for their interest in a career at Scotiabank; however, only those candidates who are selected for an interview will be contacted.  

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Location(s):  Canada : Ontario : Torrance 
Scotiabank is a leading bank in the Americas. Guided by our purpose: "for every future", we help our customers, their families and their communities achieve success through a broad range of advice, products and services, including personal and commercial banking, wealth management and private banking, corporate and investment banking, and capital markets.  
At Scotiabank, we value the unique skills and experiences each individual brings to the Bank, and are committed to creating and maintaining an inclusive and accessible environment for everyone. If you require accommodation (including, but not limited to, an accessible interview site, alternate format documents, ASL Interpreter, or Assistive Technology) during the recruitment and selection process, please let our  Recruitment team know. If you require technical assistance, please click here. Candidates must apply directly online to be considered for this role. We thank all applicants for their interest in a career at Scotiabank; however, only those candidates who are selected for an interview will be contacted.

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