Manager, Data & AI Strategy

Who We Are

Welcome to TELUS Digital — where innovation drives impact at a global scale. As an award-winning digital product consultancy and the digital division of TELUS, one of Canada’s largest telecommunications providers, we design and deliver transformative customer experiences through cutting-edge technology, agile thinking, and a people-first culture.

With a global team across North America, South America, Central America, Europe, and APAC, we offer end-to-end expertise across eight core service areas: Digital Product Consulting, Digital Marketing Services, Data & AI, Strategy Consulting, Business Operations Modernization, Enterprise Applications, Cloud Engineering, and QA & Test Engineering.

From mobile apps and websites to voice UI, chatbots, AI, customer service, and in-store solutions, TELUS Digital enables seamless, trusted, and digitally powered experiences that meet customers wherever they are — all backed by the secure infrastructure and scale of our multi-billion-dollar parent company.

Location & Flexibility

This role will operate remotely in the United States or based out of one of our major North American office locations in Charlottesville, VA, Durham, NC, Columbus, OH, or Boston, MA.

The Opportunity

The Manager, Data & AI Strategy leads the delivery of TELUS Digital's Data & AI advisory engagements, owning client relationships and driving outcomes across AI strategy, data governance, operating model design, and enterprise transformation.

This role acts as the face of the engagement—directing teams, leading workshops, and guiding clients from initial problem definition through to clear, actionable strategies and roadmaps. Managers operate as trusted advisors, bringing structured thinking, domain or capability expertise, and the ability to translate ambiguity into forward momentum.

Beyond delivery, Managers are expected to contribute to the evolution of the practice—developing reusable frameworks, shaping ways of working, mentoring junior team members, and identifying opportunities to expand client impact.

Responsibilities

Engagement Ownership & Client Partnership

• Own end-to-end delivery of client engagements, ensuring high-quality, on-time outcomes

• Serve as the primary point of contact and face of the engagement

• Build strong, trust-based relationships and act as a true partner to client stakeholders across business and technical teams

• Identify gaps between business ambition and current data/AI capabilities, and clearly articulate implications and risks

Workshops, Facilitation & Executive Advisory

• Design and lead client workshops across AI strategy, use case prioritization, governance, and operating model design

• Craft and deliver compelling, executive-level narratives that guide stakeholders from problem definition through to clear actions and outcomes

• Drive discussions that lead to alignment, decisions, and commitment to action

• Present and defend strategic recommendations to client stakeholders

• Challenge assumptions and guide clients toward pragmatic, value-driven decisions

• Advise on AI governance, responsible AI practices, and risk management approaches, including model oversight, policy design, and compliance considerations

Structuring Problems & Driving Outcomes

• Translate ambiguous client needs into structured problem statements, rigorous workplans, and high-impact deliverables

• Lead the creation and delivery of comprehensive strategic artifacts, including:

  • Maturity assessments, gap analyses, and actionable recommendations

  • Use case inventories and solution roadmaps

  • Data/AI operating models, governance frameworks, and prototype concepts

• Distinguish between foundational investments and advanced AI initiatives to ensure pragmatic sequencing, realistic phasing, and sustainable adoption

• Guarantee that all final outputs are highly actionable, prioritized, and explicitly aligned to measurable business value

Use Case Strategy, Prototyping & Solution Shaping

• Lead identification and prioritization of AI and data use cases aligned to business objectives

• Define evaluation frameworks (value, feasibility, data readiness, risk)

• Drive the creation of