Business Strategist, AI Foundation Models. US or Canada

Autodesk•Toronto, ON

9d•CA$78,000 - CA$114,400•Remote

About The Position

Autodesk is building the next generation of AI capabilities that will transform how customers design and make. The Foundation Models team develops AI technologies that operate across Autodesk’s platform and business units. This role provides the analytical foundation needed to guide adoption, monetization, cost modeling, and strategic planning for these AI investments. You will apply structured problem-solving, financial modeling, usage analytics, and cost analysis, including inference COGS and GPU economics, to inform business decisions. This role requires fluency, or the ability to ramp quickly, in cloud, GPU, and inference cost dynamics as they relate to AI product economics, COGS, scaling, and margin. Your work will clarify how foundational models scale, how they affect Autodesk’s economics, and how the company should prioritize investment and deployment across multiple product areas. The successful candidate will operate at the center of Autodesk’s AI transformation, shaping business strategy and analytics that inform AI development, technology roadmaps, and long-term priorities. They will develop structured strategic frameworks, adoption and impact forecasts, and decision-support insights that guide Autodesk’s AI portfolio and enable leadership alignment across industry groups and executive forums.

Requirements

Bachelor’s degree in a relevant field, such as Engineering, Data Science, Statistics, Computer Science, Economics, or equivalent experience
5+ years in business strategy, analytics, strategic finance, business operations, product strategy, product analytics, management consulting, or a similarly analytical role
Experience building financial, operating, or business models across adoption, usage, revenue, COGS, margin, and ROI
Strong financial modeling and analytical skills; Excel required, SQL/Python familiarity preferred
Experience with cloud, GPU, and inference cost structures, or demonstrated ability to ramp quickly on AI infrastructure economics
Ability to create structured, executive-ready presentations and strategic narratives, including management consulting-style PowerPoint decks
Demonstrated ability to operate in ambiguous environments and build frameworks from scratch
Strong communication skills with the ability to distill complexity into clarity

Nice To Haves

MBA or relevant graduate degree
Management consulting experience combined with hands-on operating experience in a technology company
Experience with AI infrastructure, usage-based monetization models, model serving, inference costs, GPU economics, cloud economics, or compute cost optimization
Experience in SaaS economics, cloud cost analysis, or AI/ML-driven products
Background in subscription analytics, pricing, packaging, or monetization strategy
Experience with AI agents and API-driven business models

Responsibilities

Build analytical and financial models that quantify the business impact of AI capabilities across Autodesk’s platform and industry groups, including Architecture, Engineering & Construction, Product Design & Manufacturing, and Media & Entertainment
Develop forecasts for AI adoption, usage, revenue influence, inference costs, margin implications, and long-term planning
Construct business cases and decision-ready analysis to guide investment, prioritization, monetization, pricing, packaging, and leadership reviews
Model and optimize cloud, GPU, and inference cost economics, including utilization, scaling behavior, cost efficiency, COGS, cost-to-serve dynamics, and margin implications
Translate product telemetry into insights supporting monetization, pricing, packaging, and cross-industry growth opportunities
Create executive- and Board-ready presentations, narratives, and slide materials that communicate strategic recommendations, investment plans, tradeoffs, and business priorities with clarity and impact
Contribute to building repeatable dashboards, reporting frameworks, and tools to enable data-driven execution across Product, Finance, and Engineering teams