Techno-Economic Analyst

Commonwealth Fusion Systems is on a mission to deliver the urgent transition to fusion energy, designing and building commercially viable fusion power plants. Since 2018 it has raised $4 billion of capital, making it the largest and leading fusion company in the world. For fusion power plants to be successful they must be economically competitive with other power generation technologies, and this Techno-Economic Analyst is responsible for developing the models critical to evaluating their financial viability and commercial desirability. The role works closely with internal CFS team members to create and evolve economic models, run sensitivity analysis, identify opportunities for improvement and provide input to design, engineering and R&D on the impacts. It also serves an important function within the business development team, supporting communication of the benefits of fusion energy to customers and key stakeholders.

Key Responsibilities

• Continue the development of techno-economic modelling at CFS to characterise the future costs and benefits of CFS products, including ARC, the world’s first commercial fusion power plant, and incorporate expectations for learning and experience benefits as the company develops additional power plants
• Establish and maintain company best practices and lessons learned on analytical methods and models, and systematically improve models and analysis over time
• Track and maintain a current assessment of ARC’s competitiveness against other energy solutions
• Work closely with the Scientific Software team to maintain the modelling tools and their underlying assumptions over time
• Collaborate with R&D, Engineering, Manufacturing, Scientific Software, System Engineers, Finance, Business Development and external stakeholders to collect primary data, populate models, productionalise them and validate model fidelity
• Prepare briefing materials, write and publish research results in internal technical reports and public-facing presentations, including collaborations with key external partners, and present and defend analysis and underlying assumptions to a wide range of audiences
• Support Investor Relations, Business Development, Corporate Finance and Engineering teams with queries and reports as needed, and support potential customers, sponsors, partners and investors in diligence processes
• Apply quantitative and qualitative research, case studies, literature reviews and expert input to identify the key variables and sensitivities that determine the costs and value of ARC
• Support the evaluation of new R&D opportunities, Open Innovation pathways, technology advancement and market opportunities, and support management in developing scope, budget and schedule for new projects
• Identify key areas of risk, uncertainty and variability within models and develop mitigation strategies with the business
• Act as a feedback loop to R&D and engineering on the key levers for driving down product costs and LCOE, synthesise results into insight on future technology R&D and design decisions, and work with procurement to refine cost estimates for CFS products and associated systems

Requirements

Essential:

• Bachelor’s degree in engineering, engineering technologies, business, finance, economics, maths, physics or a related field
• 5+ years of relevant industry experience
• Exposure to or professional knowledge of energy systems
• Experience working on quantitative analysis with probabilistic models, and skills in probabilistic modelling and forecasting
• Mastery of MS Excel and proficiency in MS PowerPoint
• Understanding of the calculation, derivation and drivers of variability of key industry metrics, including levelised cost of energy, overnight capital costs, internal rate of return and utility return on equity
• Professional knowledge of thermodynamics and engineering fundamentals, with the ability to solve analytical problems related to energy usage and plant optimisation from first principles
• Experience working in cross-functional teams and facilitating collaboration across domains
• Experience managing stakeholder relationships, including prioritising and executing disparate deliverables on tight timelines with clear communication
• Ability to work collaboratively in a team and autonomously to close gaps, and to execute mid to long-term assignments that are loosely defined
• Strong organisational ability to manage many cases and scenarios, to map out complex interconnected systems, and to apply exquisite attention to detail across a multitude of systems
• Ability to identify the right level of detail for a task, from rough, quick estimates that set development priorities to detailed, reliable models that finalise project specifications
• Adaptability to a start-up environment, with the initiative to develop own tasks and timelines and solve broad, ambiguous problems, operating with general guidance from management and increasingly taking independent ownership
• Reliable and persistent work ethic, and proven writing and communication ability appropriate for preparing material for a broad external audience
• Ability to generate work plans, define milestones for own tasks and contribute to process improvement

The role also requires the ability to perform activities such as typing, standing and sitting for extended periods, willingness to occasionally travel or work nights, weekends or on-call, and work in a facility with industrial hazards including heat, cold, noise, fumes, strong magnets, lead, high voltage, high current, pressure systems and cryogenics. This role requires compliance with U.S. Export Control Laws, and any offer of employment is contingent on that compliance.

Desirable:

• Experience with detailed process modelling of emerging technologies and techno-economic models of new technologies and systems
• Cross-functional experience in energy system engineering, business development and financial modelling
• Experience developing probabilistic models with a focus on energy system modelling and optimisation
• Comfort with mathematical modelling and tool development in Python