Machine learning jobs

AI & Machine Learning Jobs in Renewable Energy

AI and machine learning roles in renewable energy build, train, and run the models behind generation forecasts, battery dispatch, fault detection, and pricing; the models are the job itself, not an office tool used alongside it.

The sector is short of these people. The IEA's Energy and AI report finds that AI-related skills are much less common in the energy sector than in other industries, even as it estimates that AI-based grid tools could unlock up to 175 GW of transmission capacity without a single new line being built. That gap between what the models could do and the number of people able to deploy them explains most of the hiring in this category.

Where machine learning pays its way

Forecasting remains the clearest commercial case. A wind or solar operator that can predict tomorrow's output with confidence can sell it in the day-ahead market instead of paying for imbalances later. In 2019, DeepMind and Google reported that predicting output 36 hours ahead for 700 MW of US wind capacity raised the value of that energy by roughly 20%. Forecasting models have since become standard equipment for traders and asset owners, and the work has moved from proving the idea to keeping hundreds of models accurate as fleets, weather, and market rules change.

Battery storage opened a second front. A battery earns money by charging and discharging at the right moments across wholesale, balancing, and ancillary markets, and optimisation models decide those moments every few minutes. The same logic now runs inside home energy platforms that control a household's battery, heat pump, and EV charger against a dynamic tariff. Asset health is the third: models that read vibration, temperature, and SCADA data to catch a failing gearbox bearing or a degrading inverter before it trips. The IEA puts the effect of AI-based fault detection on grid outages at a 30 to 50% reduction in duration.

Who is hiring

Data Scientist is by far the most common title in our listings, followed by Senior Data Scientist, Machine Learning Engineer, AI Engineer, and a growing number of hybrid posts such as Forward Deployed Engineer for AI automation. London and Hamburg lead the locations, with Glasgow, Berlin, Lisbon, and Bristol close behind.

The employers fall into three groups. Software-led retailers and energy platforms, such as Rabot Energy in Hamburg, OVO Energy, and Arcadia, use machine learning for dynamic pricing, consumption forecasting, and controlling customers' devices. Large generators and utilities, including NextEra Energy, EDP Renewables, Scottish Power, and AGL Energy, hire for portfolio forecasting and asset analytics. Equipment makers such as Siemens Energy and the thermal battery developer Antora Energy apply models to product performance and manufacturing.

What employers screen for

General ML credentials rarely get a candidate far on their own. Hiring managers want production experience with time-series and probabilistic forecasting, plus enough power-system knowledge to know what curtailment is, why imbalance prices spike, and what makes up a battery's revenue stack. Candidates coming from data science or data engineering in other industries can pick up the power-system side on the job; those arriving from energy trading bring the market intuition and need the engineering discipline.

The demand side is growing too. The IEA expects data centre electricity use to more than double from 415 TWh in 2024 to around 945 TWh by 2030, which puts people who understand both the models and the power system in an unusual position: they work on the load and on the grid that has to carry it.