AVP, Analytics, Insights and AI, AI Product

Work Location: Toronto, Ontario, Canada

Hours: 37.5

Line of Business: Data & Analytics

Pay Details: $155,000 - $215,000 CAD

TD is committed to providing fair and equitable compensation opportunities to all colleagues. Growth opportunities and skill development are defining features of the colleague experience at TD. Our compensation policies and practices have been designed to allow colleagues to progress through the salary range over time as they progress in their role. The base pay actually offered may vary based upon the candidate's skills and experience, job-related knowledge, geographic location, and other specific business and organizational needs.

As a candidate, you are encouraged to ask compensation related questions and have an open dialogue with your recruiter who can provide you more specific details for this role.

Job Description

The AVP, AI Product leads a material enterprise AI product area or AI transformation initiative within Layer 6. This role is accountable for translating TD's AI priorities into trusted, scalable products and measurable outcomes - from opportunity selection and AI product strategy through delivery, production, adoption, day 2 management and controlled retirement. They will lead a team of AI product owners and partners with multidisciplinary teams across business, design, technology, and risk to create AI powered experiences that are remarkably human and refreshingly simple.

Role Accountabilities

• Set product direction: Collaborate with business and technology partners to help shape the target state vision, develop AI roadmap, align on business outcomes and set investment priorities for the assigned AI product area or portfolio.
• Select the right opportunities: Assess client or colleague needs, strategic fit, AI suitability, feasibility, economics, risk and reuse potential; Surface and facilitate stop or pivot decisions as needed.
• Own AI delivery end to end . Lead discovery, design, build / blend, evaluation, control readiness, launch, adoption, production operation, scaling and retirement of AI capabilities.
• Support realization of measurable value . Set baselines and success measures; track adoption, financial and non-financial benefits, product economics and sustained outcomes with business owners.
• Apply technical product judgment on model, data, retrieval, agentic design, evaluation, architecture, integration, human oversight, reliability, latency, scalability and cost decisions.
• Embed Responsible AI and controls . Ensure model risk, privacy, security, compliance, data governance, operational resilience and Responsible AI requirements are addressed through appropriate evidence, monitoring, escalation and remediation. Operate within TD risk appetite and governance.
• Own production health . Establish quality, service and risk thresholds; oversee monitoring, incidents, change, rollback, issue management and controlled decommissioning.
• Scale through reuse . Drive adoption of reusable capabilities, control artifacts, evaluation assets, reference patterns and playbooks to simplify delivery and reduce duplication.
• Lead adoption and change . Embed AI into business workflows, clarify accountable use, enable users, gather feedback and sustain client and colleague outcomes.
• Build and lead a high performing team . Hire, coach, develop and performance-manage direct reports; build succession; align senior business, technology and control partners; remove barriers and raise product standards

REQUIRED QUALIFICATIONS

• Bachelor's degree or equivalent practical experience in a relevant discipline.
• 10+ years of progressive experience across product management, technology delivery, AI/data/analytics, platform leadership or transformation, including significant leadership of complex products from discovery through production and sustained operation.
• Demonstrated people leadership, including hiring, coaching, performance management, development and succession of direct reports.
• Strong product and commercial judgment: strategy, roadmaps, prioritization, business cases, metrics, adoption and value realization.
• Practical fluency in predictive AI, GenAI and agentic systems, including LLMs, RAG, evaluation, orchestration/tool use, human oversight, model/data lifecycle and observability.
• Ability to constructively challenge architecture, integration, cloud/platform, API, reliability, security and cost trade-offs without being the principal engineer or model developer.
• Experience influencing senior executives and leading across business, technology, operations and control functions in a regulated environment.

PREFERRED QUALIFICATIONS

• Master's degree in computer science, engineering, data science, business, finance or a related field.
• Financial-services experience and familiarity with model risk, Responsible AI, privacy, cybersecurity, data governance, third-party risk and operational resilience.
• Experience scaling enterprise AI platforms, reusable AI capabilities, MLOps/LLMOps, evaluation tooli