Director, Data

Are you looking to join a world-class manufacturing organization? At Husky TechnologiesTM, we strive to be the best with a strong foundation built on innovation, collaboration and a unique culture with great people. If you are attracted to bold goals, believe in uncompromising honesty, support mutual respect, care about environmental responsibility, have a passion for excellence and a desire to make a positive contribution – we want you to join the Husky TechnologiesTM team

What we offer

• Excellent benefits package and retirement savings plans as well as life insurance program
• Competitive vacation policy promoting work-life balance
• Challenging career opportunities and growth
• Opportunity to work, innovate and collaborate with passionate people who drive change
• Amazing team – we are the best in the world at what we do

Husky TechnologiesTM Campus

• Large, beautiful campus with clean, state-of-the-art air-conditioned offices and manufacturing facilities with high air quality, climate control and outstanding safety records
• Onsite fitness and wellness center
• Organized out- and indoor sports activities such as running teams, and bicycle teams
• Access to onsite medical practitioners
• Onsite cafeterias with fresh and healthy meal options
• Free parking
  

Job Description

Husky is accelerating its use of AI, analytics and data platforms to improve decision speed, operational efficiency, customer experience and cost transparency. We are seeking a Director of Data & AI who combines hands-on technical depth, product-minded delivery, business partnership and pragmatic governance.

The role leads the global Data & AI Office and works in a hub-and-spoke model with business leaders, AI Champions, process owners, architecture, cybersecurity, finance and operations. The Director is accountable for turning data and AI opportunities into measurable business outcomes while ensuring data quality, security, compliance, responsible AI and ITGC-aligned delivery.

This is not a pure policy or reporting role. The successful candidate must be comfortable operating close to the technology, challenging complexity, enabling rapid pilots, scaling what works and building the standards required to make AI safe, repeatable and enterprise-ready.

Responsibilities

Delivery, Product and Business Value

• Build and run a portfolio of AI, analytics and data products, prioritizing initiatives with clear business value and measurable outcomes.
• Lead rapid experimentation and scale successful pilots into stable, reusable enterprise capabilities.
• Partner with business leaders and process owners to identify where AI and trusted data can remove friction, improve decisions or redesign work.
• Define value cases, adoption metrics and benefit-realization methods for AI and data initiatives.
• Create reusable delivery patterns for AI agents, analytics products, predictive models, knowledge solutions and automation.

Hands-On Technical Leadership

• Remain close to the technical work: review architectures, data models, integration approaches, AI agent designs, prompt/RAG patterns, ML use cases and platform decisions.
• Guide teams on modern cloud data platforms, data warehousing, data products, analytics engineering, Python/SQL practices, MLOps and GenAI solution design.
• Work with architecture, cybersecurity and infrastructure teams to ensure AI/data solutions are secure, scalable and operationally supportable.
• Challenge over-engineering and promote pragmatic designs that can deliver business value quickly without creating long-term technical debt.

Data Strategy, Governance and Quality

• Develop and execute the enterprise data strategy aligned with business priorities and AI readiness.
• Establish data ownership, stewardship, quality standards, metadata practices and trusted-data principles across the organization.
• Improve data quality, lineage, accessibility and consistency across core business domains and platforms.
• Ensure compliance with applicable data protection, security, privacy, retention and industry requirements.

AI Governance and Responsible AI

• Lead practical AI governance that enables responsible innovation rather than slowing delivery.
• Define standards for AI use-case intake, risk assessment, knowledge-source validation, model/agent review, access control, monitoring and periodic review.
• Ensure AI initiatives meet responsible AI, cybersecurity, privacy, IP protection, auditability and ITGC expectations.
• Partner with Legal, HR, Compliance,