Quantitative Researcher

Fuse Energy is a forward-thinking renewable energy startup on a mission to deliver a terawatt of renewable energy - fast.
We're combining first-principles thinking with cutting-edge technology to build a radically better energy system.
We raised $170M from top-tier investors including Multicoin, Balderton, Lakestar, Accel, Creandum, Lowercarbon, Ribbit, Box Group and strategic angels like Nico Rosberg, the Co-Founder of Solana and GPs behind Meta, Revolut, Spotify, Uber and more.
We’re creating a fully integrated energy company: from developing solar, wind and hydrogen projects to real-time power trading and distributed energy installations.
By selling directly to consumers, we cut out the middleman, lower costs and pass on savings to customers.
But we’re not stopping there.
We’re also building the Energy Network: a decentralised platform of smart devices that rewards users in Energy Dollars for electrifying their homes, shifting usage to off-peak hours, and helping balance the grid.
This network strengthens grid stability - a critical foundation for scaling AI data centers and other energy-intensive industries.
Responsibilities Develop and implement quantitative models to support hedging strategies for retail energy supply Analyze market data to optimize the performance of physical assets such as solar and wind farms Conduct statistical and scenario-based modeling to inform trading and risk management decisions Monitor power markets, pricing trends, and regulatory developments to identify risks and opportunities Collaborate with engineering and operations teams to integrate models into day-to-day decision-making Competitive salary and an equity sign-on bonus Biannual bonus scheme Fully expensed tech to match your needs Paid annual leave Breakfast and dinner allowance for office based employees 1+ years of experience in quantitative modeling, data analysis, or a related role in energy, trading, or analytics Strong proficiency in Python, with experience using libraries for data analysis, modeling, and visualization Solid understanding of statistical modeling, optimization, and analysis techniques Experience working with time series or market data, preferably in energy or commodities markets Strong problem-solving skills and the ability to translate complex data into actionable insights