Associate Director-AI&Data

We Are

Accenture Data & AI — the people who love using data to tell a story. We're the world's largest team of data scientists and experts in machine learning and AI. A great day for us means solving big problems using the latest tech, serious brain power, and deep knowledge of just about every industry. We believe a mix of data, analytics, automation, and responsible AI can do almost anything — spark digital reinvention, widen the range of what humans can do, and breathe life into smart products and services.

You Are: A thought leader and practitioner at the intersection of AI strategy and data delivery — As a senior leader in the Data & AI practice, you will coach and mentor up-and-coming leaders, guide diverse multi-disciplinary teams, and help clients embed AI and data capabilities as a lasting part of their organisational culture — with a strong focus on responsible AI, data governance, and public trust

An executive who never stopped being a practitioner. You have built and shipped AI systems that run in production, and you are as credible in a technical design review as in a board-level investment conversation. You have led at scale — teams, portfolios, commercial results — and you can tell a CEO why their AI ambition will stall against their existing systems, then show them what to do about it.

What You'll do

Role Summary

You will own Accenture's AI agenda in the Québec market: what we take to clients, how we deliver it, and the capability that sustains it.

Clients here are not short of AI ambition. They are short of the foundation underneath it — systems nobody fully understands, knowledge that lives in people rather than in artifacts, architectures never designed for autonomous software to act inside them, and no safe way to let AI touch a system of record. Closing that gap is the work: making the existing enterprise legible to AI, designing the architecture and controls that let AI be trusted with consequential decisions, encoding the domain meaning that makes a solution reflect the business, and building systems that hold up in production.

It also means making it last. You will help clients set the architecture and operating model their AI estate will run on, prove and improve those systems once live, keep the economics defensible to a CFO, and deploy privately or sovereignly where data cannot leave their control. The standard we are accountable for is AI that is enterprise-grade: in production, measurable, economically sustainable and defensible to a regulator.

Key Responsibilities

Grow the Québec AI business

Own the market strategy, value propositions and commercial constructs for AI in Québec, adapted to local industry priorities, regulatory context and buying behaviour — in Québec-ready form, not translated global collateral.
Develop and personally hold executive relationships (CEO, CIO, CTO, CDAO, COO, CFO and business-unit leaders) across priority accounts.
Lead the shaping of proposals and RFP responses; own solution architecture, estimating, commercial construct and risk posture on the pursuits you lead.
Carry an originated-sales objective and contribute to managed revenue growth across the portfolio.

Lead delivery

Lead agentic and generative AI systems end to end: value case, architecture, model and pattern selection, evaluation design, integration with systems of record, deployment and transition to run.
Lead the work of recovering what a client's estate actually does and what the organization actually knows — legacy code, undocumented logic, institutional knowledge — and making it usable by AI systems.
Establish the architecture and control patterns that let autonomous systems operate against production environments with appropriate isolation, authorization, traceability and oversight.
Ensure domain meaning is engineered deliberately — ontology, semantics, business context — rather than approximated.
Lead the hardest engagements personally, including forward-deployed work where small, senior, embedded teams work directly against client outcomes.
Own quality, risk, margin and delivery health across the Québec AI portfolio, including architecture review on engagements you do not lead.

Industrialize and sustain

Establish the reference architectures, engineering standards and operating patterns that take clients from isolated solutions to a governed AI estate: lifecycle management, evaluation and regression discipline, observability, and continuous optimization once live.
Bring economic discipline to AI at scale — unit economics, tiered model selection, cost attribution and consumption controls.
Codify what we learn into reusable accelerators and delivery playbooks.
Responsible AI, privacy and sovereignty
Embed responsible AI practice into every engagement: risk classification, evaluation and red-teaming, bias and safety testing, traceability, human oversight and auditability.
Ensure solutions meet Québec and Canadian obligations, including Law 25, privacy impact assessment practice, data residency expectations, and applicable sectoral supervision such as model risk and third-party risk in financial services.
Lead client conversations on private and sovereign AI, including the architectural and economic trade-offs of keeping models, data and inference inside client or national boundaries.
Advise on quality and fairness in French-language AI systems: model performance in Québec French, terminology and register, and the service-language obligations that apply to our clients' own customers.
Build the team
Build, lead and retain a bilingual team of AI engineers, architects, data scientists and delivery leads; own capability planning, skills strategy and career progression for the Québec AI community.
Recruit from the Québec talent market and partner with universities and research institutes on talent pipeline and applied research.
Stay close enough to the technology to review architecture, critique evaluation design and challenge weak assumptions.

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