Principal Analyst – Large Loads

We are looking for a Principal Analyst to join Wood Mackenzie’s Power & Renewables group, covering Large Loads (data centers, advanced manufacturing, and facility electrification). The P&R team consists of leading analysts covering some of the most cutting-edge topics in the energy transition, including data centers, electrification, microgrids, and utility modernization. Our research and thought leadership inform the strategies of key players throughout the energy industry, driving innovative business models while hastening the energy transition. The Principal Analyst will author research and client-facing insights on large loads — covering regulatory developments, market dynamics, and technology trends — and contribute to the team's long-term demand forecast. They will also be a primary expert-reference for Wood Mackenzie's product and data teams for tool-related conversations — understanding what customers need from our tools, translating that into concrete use cases and requirements, and partnering with the product and data science teams to validate our roadmap. Qualified applicants will have deep, current expertise in the data center and large load space. We are prioritizing candidates with strong AI infrastructure expertise: a working understanding of compute (chip types, training vs. inference workloads, and how compute demand translates into power and siting requirements), the distinction between DC and AC power infrastructure within data center design, and the technical, commercial, and policy factors driving data center demand growth. Responsibilities • Author research reports and one-off insights (regulatory changes, market insights, technology advancements) and contribute to the long-term demand forecast assumptions. • Serve as subject matter expert on data center technology, policy, siting, and energy supply for large loads; provide authoritative technical and market context to product and data teams. • Serve as the team’s go-to expert on AI infrastructure – compute trends, DC vs. AC power architecture, and the factors driving data center demand – informing both research and product. • Participate in customer and client conversations to gather feedback, use cases, and data requirements; represent Wood Mackenzie research in these product-facing conversations. • Guide and QA the work of data analysts and data scientists in classifying, extracting, and vetting large-load data from documents and other sources. • Support Sales and Marketing in existing or new client conversations/presentations and internal webinars. • Represent Wood Mackenzie at client meetings, conferences, and other Wood Mackenzie offices as needed. • Support Wood Mackenzie's consulting team on large load-related engagements as needed. Qualifications • Demonstrated expertise in AI infrastructure: compute (chip types, training vs. inference workloads, and how compute demand translates into power and siting requirements), the distinction between DC and AC power infrastructure within data center design, and the technical, commercial, and policy factors driving data center demand growth. • 7+ years of industry or research experience in the power sector, with substantial, recent, hands-on exposure to data centers or other large electricity loads. • A track record of authoring research or client-facing analysis (reports, forecasts, or similar). • Demonstrated experience translating market or customer needs into product or data requirements, ideally through direct client- or stakeholder-facing work. • Comfort partnering with product, data science, or other non-research teams in ongoing, iterative workflows (standups, refinement sessions, and similar). • A Bachelor's or Master's degree, preferably in engineering, energy, economics, finance, or policy. • Excellent written and oral communication skills; ability to represent Wood Mackenzie externally with clients and at conferences. • Strong organizational and project management skills — able to drive forward a workstream with multiple collaborators and moving parts. • Ability to leverage AI tools for efficiency without compromising the correctness of the final data or product deliverables.