Career With Ellume
Applied AI Scientist
Experience: Hands-on modelling experience (work, research or projects)
Who we are
At Ellume, we are pioneering the future of clean power. We operate across three verticals—Renewable Energy, Independent Power Production (IPP), Engineering, Procurement & Construction (EPC), and Technology—committed to driving innovation across the renewable sector.
Our Technology vertical builds software-driven solutions that help operators maximise the efficiency and compliance of their renewable assets, including Ellume360 (a unified OS for Solar, Wind, and BESS), Ellume VECTOR (physics-aware AI for predictive yield), and Ellume BRIDGE (an automated NERC GADS compliance engine).
We are always looking for talented, motivated people eager to contribute, grow, and make a lasting impact. If you are ready to be part of a forward-thinking team building the future of renewable energy infrastructure, we want to hear from you.
What we're looking for
Building predictive models on operational data, with honest uncertainty around every prediction.
Spotting underperformance and faults in inverter and SCADA data, and telling real problems apart from sensor noise.
Working out where energy is lost, whether to soiling, degradation, downtime or curtailment.
Turning messy sensor data into reliable features, and evaluating models properly, without leakage.
Taking models into production, keeping an eye on drift, and measuring whether they actually help operators.
Using LLMs where they make sense, such as summarising maintenance logs or answering questions about asset data.
Applicant requirements
A Bachelor's or Master's in Statistics, Mathematics, Computer Science, Physics or a related field.
Hands-on experience building models on real-world data, whether at work, in research or through your own projects.
Strong fundamentals in probability, statistics, linear algebra and optimisation.
A solid grasp of classical machine learning, including when and why models fail.
Practical deep learning experience in PyTorch or TensorFlow, including transformer architectures.
Experience with time-series data and forecasting.
Strong Python and SQL, and clean, version-controlled code.
Nice to have: experience with LLMs, RAG and vector databases; Bayesian methods or causal inference; MLOps tools for deployment and monitoring; a background in energy, utilities or IoT.