A
Autodesk
Data Science AI Strategy
Skills
SQL QueriesPythonProduct StrategyData ScienceData AnalysisMachine LearningAIWorkdayManagement Consulting
23 days ago
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Important information
Contract type:
Permanent
Location:
London, UK
Work mode:
Onsite
Published on:
2 October 2026
What they need
- 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 cost 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 foundation 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
- 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 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
- Operate as a self-starter with the ability to work independently, take ownership, and drive work forward in an ambiguous environment- Demonstrated ability to navigate ambiguity, structure complex problems, and build frameworks from the ground up
- Strong financial modeling and analytical skills; Excel required, with SQL or Python familiarity preferred
- Ability to create structured, executive-ready presentations and strategic narratives, including management consulting-style PowerPoint decks
- Strong communication skills with the ability to translate complex analysis into clear, actionable insights
- Experience with cloud, GPU, and inference cost structures, or demonstrated ability to ramp quickly on AI infrastructure economics
- 5+ years of experience in business strategy, analytics, strategic finance, business operations, product strategy, product analytics, management consulting, or a similarly analytical role
- Masters or Bachelor’s degree in a relevant field, such as Engineering, Data Science, Statistics, Computer Science, Economics, or equivalent experience
- Self-starter with the ability to work independently, take initiative, and drive work forward
- Experience building financial, operating, or business models across adoption, usage, revenue, COGS, margin, and ROI
- Ability to work effectively across multiple stakeholders and cross-functional teams
- BS or MS in Data Science or a related field, with 3–5 years of relevant experience depending on educational background
- 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
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