Managing Director, Data and Ai

**Being part of Air Canada is to become part of an iconic Canadian symbol, recently ranked the best Airline in North America. Let your career take flight by joining our diverse and vibrant team at the leading edge of passenger aviation.**

The Managing Director (MD) of Data & AI is a critical executive leadership role reporting to the Chief Digital Officer, accountable for defining and operationalizing Air Canada’s enterprise data and AI strategy. This leader will shape how data is collected, governed, enriched, and activated to enable smarter decision-making, operational excellence, commercial impact, and personalized customer experiences. As a strategic thought partner to the business, the Managing Director of Data & AI will architect and scale enterprise data platforms, AI capabilities, and operating models that drive measurable business value.

A key objective of this role is to build world-class in-house capabilities in data product management, data engineering, and applied AI, reducing dependency on third-party vendors while enhancing speed, accountability, and innovation. This leader will define the organizational structure, talent strategy, and operating model needed to enable a scalable, high-performing data function aligned with our shift toward a product and platform operating model.

We are entering a transformative era where data and AI will define the winners and losers in every industry. Generative and agentic AI represent the next leap in how enterprises operate, serve customers, and generate value. For Air Canada, this role is not only technical, but it is also strategic. The Managing Director (MD) of Data & AI will be instrumental in shaping the airline’s future, helping us reimagine what’s possible in travel. This role is a mission-critical position at the forefront of innovation, competitiveness, and digital reinvention.

**This position may be located in Montreal or Toronto.**

**Responsibilities**:
**Data & AI Strategy & Governance**
- Define and lead the execution of Air Canada’s enterprise Data & AI strategy, ensuring alignment with corporate priorities, customer needs, regulatory requirements, and emerging technologies such as GenAI and agentic platforms.
- Develop a multi-year transformation roadmap for data and AI, prioritizing high-impact, high-ROI initiatives across customer experience, operational performance, commercial optimization, and safety.
- Establish and lead the enterprise Data & AI Governance program, including policies, frameworks, and standards for data stewardship, data quality, metadata, privacy, and ethical AI usage.
- Collaborate with Legal, Privacy, Cyber security, and Risk to ensure the trustworthy, compliant, and secure use of data and AI across the organization.
- Champion a data-driven culture, embedding the use of analytics and AI into decision-making at all levels.

**Data Engineering & Architecture**
- Lead the design and build of scalable, cloud-native data platforms using technologies such as Databricks, Snowflake, and modern xOps tooling.
- Drive the consolidation and modernization of data ecosystems—streamlining ingestion, processing, storage, access, and integration across structured and unstructured data.
- Architect resilient, cost-effective data pipelines and feature stores to support AI workloads, including ML, RAG (retrieval-augmented generation), and other real-time or agentic use cases.
- Ensure data platforms are high-performing, reliable, and optimized for speed, scale, and cost efficiency in collaboration with IT Ops and Infrastructure teams.

**Data Science & AI**
- Oversee the development, deployment, and operationalization of ML models and GenAI solutions, including robust MLOps capabilities for versioning, monitoring, and retraining.
- Guide AI use case development across customer personalization, pricing optimization, operational forecasting, crew planning, and disruption management.
- Establish and manage AI Centers of Excellence and cross-functional squads to accelerate experimentation, productionization, and business value realization.
- Ensure that all AI initiatives adhere to responsible AI principles and deliver sustained value without bias, drift, or performance degradation.

**Data Product Management**
- Establish a product mindset in the data organization by creating a portfolio of reusable, high-value data products tailored to internal business units.
- Define ownership, KPIs, SLAs, and roadmaps for key data assets such as customer 360, operations performance dashboards, and commercial intelligence feeds.
- Collaborate closely with business units, product managers, and analysts to ensure the right data products exist to support self-service insights, automation, and personalization.

**Data Platforms**
- Build and lead the evolution of modern data and AI platforms, including integration of vector databases, semantic layers, real-time data APIs, and knowledge graphs to power next-gen agentic capabilities.
- Define build-buy-p