Data Science, AI Compute Strategy, Canada

Position Overview

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, GPU economics, 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, GPU cost economics, GPU utilization, and compute efficiency, revenue-impact modeling and AI strategy 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 foundation models scale, how GPU and inference
costs, revenue-impact modeling and AI strategy 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, GPU and inference cost models, and decision-support insights that guide Autodesk’s AI portfolio and enable leadership alignment across industry groups and executive forums.

Location: We are open to candidates in Canada, either hybrid or remote.

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, GPU consumption, 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 GPU economics, cloud compute costs, and inference economics, including GPU utilization, capacity, scaling behavior, cost efficiency, COGS, cost-to-serve dynamics, and margin implications

• Analyze GPU demand, utilization, and unit economics to identify opportunities to improve infrastructure efficiency and support scalable AI deployment

• Translate product telemetry and infrastructure usage data into insights supporting monetization, pricing, packaging, GPU cost management, and cross-industry growth opportunities

• Assist in creating executive presentations, narratives, and slide materials that communicate strategic recommendations, revenue-impact modeling and AI strategy, GPU and compute investment plans, tradeoffs, and business priorities with clarity and impact

• Collaborate across multiple stakeholders and cross-functional teams

• Contribute to building repeatable dashboards, reporting frameworks, and tools to track AI adoption, GPU utilization, inference costs, COGS, and business performance

• Operate as a self-starter with the ability to work independently, take ownership, and drive work forward in an ambiguous environment

Minimum Qualifications

• Bachelor’s degree in a relevant field, such as Engineering, Data Science, Statistics, Computer Science, Economics, or equivalent experience

• 5+ years of experience 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, GPU or compute costs, COGS, margin, and ROI

• Strong financial modeling and analytical skills; Excel required, with SQL or Python familiarity preferred

• Experience with GPU economics, cloud infrastructure economics, inference cost structures, compute utilization, or AI infrastructure cost modeling, or demonstrated ability to ramp quickly in these areas

• Ability to analyze GPU utilization, scaling behavior, cost efficiency, and cost-to-serve dynamics as they relate to AI products

• Ability to create structured, presentations and strategic narratives PowerPoint decks

• Demonstrated ability to navigate ambiguity, structure complex problems, and build frameworks from the ground up

• Demonstrated ability to collaborate across multiple stakeholders and cross-functional teams, align differing priorities, and drive decisions forward

• Strong communication skills with the ability to translate complex technical and financial analysis into clear, actionable insights

• Self-starter with the ability to work independently, take initiative, and drive work forward

Preferred Qualifications

• BS or MS in Data Science or a related field, with 3–5 years of relevant experience depending on educational background

• Experience with GPU economics, AI infrastructure, model serving, inference costs, cloud economics, GPU utilization, capacity planning, or compute cost optimization

• Experience analyzing the economics of GPU-intensive AI workloads, including utilization, scaling, unit costs, and margin implications

• Experience in SaaS economics, cloud cost analysis, or AI/ML-driven products

• Background in subscription analytics, usage-based monetization, pricing, packaging, or monetization strategy

• Experience with AI agents and API-driven business models