Scrum Master - Finance Data Delivery Manager – Analytics, Insights & Artificial Intelligence
Work Location: Toronto, Ontario, Canada
Hours: 37.5
Line Of Business: Enterprise Enabling Functions
Pay Details: $96,900 - $136,800 CAD
This role is eligible for a discretionary variable compensation award that considers business and individual performance.
Job Description Build the future of Finance Data & AI deliveryWe’re looking for a high‑impact Scrum Master / Finance Data Delivery Manager to help accelerate Finance’s data, analytics, and artificial intelligence agenda. This role sits at the intersection of Agile delivery, finance transformation, and emerging technology
, enabling teams to deliver meaningful outcomes in a complex, regulated environment.
If you thrive on removing obstacles, aligning stakeholders, and helping teams deliver high‑quality data and AI solutions at scale, this is an opportunity to make a real enterprise impact.
What You’ll Do Agile & Delivery Leadership- Serve as Scrum Master and Delivery Manager for Finance Data, Analytics, and AI initiatives
- Champion Agile and Lean practices aligned to enterprise delivery standards
- Facilitate all Agile ceremonies including sprint planning, stand‑ups, reviews, retrospectives, and PI planning
- Enable high‑performing, self‑organizing teams while fostering psychological safety and shared accountability
- Drive continuous improvement through delivery metrics, insights, and retrospectives
- Lead delivery of strategic finance data and analytics initiatives, ensuring alignment to business priorities
- Support AI and advanced analytics programs, including iterative experimentation, model development, and deployment workflows
- Work with Stakeholders to translate complex data and AI work into clear, value‑driven Agile increments
- Navigate ambiguity inherent in emerging technologies while maintaining delivery discipline
- Manage cross‑team dependencies, risks, and impediments across data and technology work streams
- Partner closely with Product Owners, pillar leads, and executive stakeholders to define and prioritize backlogs and roadmaps
- Align delivery execution with finance data strategy, governance models, and regulatory requirements
- Support delivery controls, checkpoints, and reporting in a risk‑ and control‑focused environment
- Ensure transparency, accountability, and timely escalation of delivery issues
- Proven experience (minimum 6 years) as a Scrum Master, Agile Delivery Lead (not just participating on Agile teams)
- Strong working knowledge of Scrum, Kanban, and scaled Agile frameworks (e.g., SAFe)
- Experience with hands‑on facilitation of all core Agile ceremonies (planning, stand‑ups, reviews, retrospectives, PI planning)
- Demonstrated success delivering finance, data, analytics, or transformation initiatives in a large organization
- Experience of operating in financial services or other regulated environments
- Ability to deliver against strategic roadmaps, not just isolated projects
- Ability to translate complex finance, regulatory, or data concepts into clear visual narratives (Use of PowerPoint/ Power BI etc.). Experience building executive‑ready decks, decision briefs, and transformation updates with a strong sense of visual design (clean layouts, appropriate charts, consistent formatting, clarity of message)
- Preferred experience with artificial intelligence (AI) applications in finance data, including the use of Python and SQL for data analysis, automation, and insight generation.
- What is NOT required
- Deep coding expertise (Python/SQL are exposure‑level, not core)
- Hands‑on model development
- Technical architecture ownership
- People‑manager responsibilities
- Experience supporting delivery of data platforms, analytics, AI, or machine learning initiatives
- Familiarity with governance, controls, and lifecycle considerations for analytics and AI solutions
- Ability to bridge conversations between business, data, and technology teams
- Experience supporting AI, ML, or advanced analytics initiatives
- Familiarity with iterative experimentation, model lifecycle checkpoints, or AI governance concepts
- Ability to translate complex analytics work into Agile increments
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