Lead competitive assessments of AI accelerators, CPUs, memory, networking, storage, and rack-scale infrastructure supporting Azure's Maia, Cobalt, and AI systems roadmaps. Evaluate competitor and industry roadmaps across architecture, process technology, performance, power, memory, system configuration, deployment timing, and supply-chain dependencies. Develop analytical models that connect product specifications, workload performance, power, cost, and utilization to assess platform competitiveness and total cost of ownership. Synthesize inputs from engineering teams, performance measurements, financial models, suppliers, market research, and public information into a consistent, decision-ready view of the competitive landscape. Identify key technology and product decision points across silicon architecture, memory, packaging, networking, and system design, and assess their implications for future Azure products. Partner with architecture, engineering, workload, performance, finance, sourcing, and product-planning teams to evaluate tradeoffs and align assumptions across organizations. Translate complex and incomplete information into clear insights, scenarios, and recommendations for product teams and senior leaders. Establish repeatable analyses, dashboards, and review mechanisms that improve the consistency, transparency, and reuse of competitive, performance, and cost information. Bachelor's Degree AND 5+ years experience in product/service/program management or software development OR equivalent experience. Master's Degree in Electrical Engineering, Computer Engineering, Computer Science, Business Administration, Finance, or a related field. 8+ years of experience in cloud infrastructure, semiconductors, AI systems, datacenter hardware, product strategy, competitive analysis, or management consulting. Experience evaluating complex technology products or platforms and translating technical information into product, business, or investment recommendations. Experience applying structured problem-solving approaches to complex technology or business questions, including developing hypotheses, conducting quantitative and qualitative analysis, and translating findings into actionable recommendations. Experience with quantitative analysis, such as performance benchmarking, cost modeling, total cost of ownership, market analysis, capacity analysis, or financial modeling. Experience collaborating across technical and business functions, such as engineering, architecture, finance, sourcing, operations, or product planning. Ability to communicate complex technical and business concepts clearly to technical teams, business partners, and senior leaders. Understanding of one or more infrastructure domains, including AI accelerators, CPUs, high-bandwidth memory, advanced packaging, networking, storage, server systems, or rack-scale architecture. Familiarity with semiconductor development and supply chains, including foundry processes, memory, packaging, substrates, assembly and test, and hardware manufacturing. Experience analyzing hyperscale cloud platforms or hardware ecosystems. Experience connecting architecture and workload performance to system-level power, cost, utilization, and total cost of ownership. Experience in management consulting, corporate strategy, or technology strategy, preferably focused on cloud infrastructure, semiconductors, AI systems, or datacenter technologies. Demonstrated ability to combine technical understanding with market, competitive, financial, and strategic analysis. Experience creating executive-level analyses and recommendations that have influenced product roadmaps, investment decisions, or technology strategy.