Hiring nowEnergy, grid and utility intelligence

Renewable Forecasting Scientist

Also called Renewables ML Scientist

A Renewable Forecasting Scientist predicts wind and solar output and says how uncertain each prediction is. Companies use this to plan dispatch, trade with confidence and keep constrained grids stable.

What they do

The work starts with weather inputs and plant telemetry. The scientist builds models that forecast generation for wind and solar sites across different horizons. They clean noisy meter data, handle curtailment and outages, and separate real signal from sensor faults. They produce prediction intervals, then test whether those intervals hold up. They run backtests across calm, stormy, cloudy and clear periods. They work with operations and trading teams to learn which errors cost most. They also feed forecasts into grid simulators and optimisation systems, and they investigate when a forecast misses badly.

What a strong hire delivers

In the first months, a strong hire delivers a forecasting baseline that the business trusts and can measure. They set up rolling evaluation so error is visible by weather regime. They produce intervals that are calibrated, not decorative. They fix the worst data problems in telemetry. They hand operations a clear account of when the forecast is reliable and when it is not, and they put a review routine in place for large misses.

How you can tell they are good

Ask them to walk through a forecast that failed and what they changed afterwards. A strong candidate talks about regimes, data quality and calibration without prompting. They can show coverage results, not only headline error. They explain how a forecast feeds a decision, such as dispatch or reserve planning. Be wary of anyone who reports a single average error and treats uncertainty as an afterthought.

Why it is hard to fill

The title is rarely used consistently. The same work sits under energy analytics, power systems research or applied ML. Few people combine modern time-series modelling with real knowledge of plant behaviour, curtailment and grid operations. Most candidates lean one way: strong ML with little energy sense, or strong energy domain knowledge with limited modelling depth. Good people are usually settled in utilities, developers or research groups.

Where to find them

Look in renewable developers, grid operators, utilities, energy traders and independent forecasting providers. Typical titles include energy analyst, power systems engineer, data scientist in energy, and research scientist in meteorology or applied statistics. Strong candidates also come from control engineering, scientific computing and simulation teams who have moved toward forecasting. Adjacent talent exists in weather and climate modelling groups.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for a Renewable Forecasting Scientist

  1. Tell us about a wind or solar forecast you built. What did it predict, and how did you judge it?

    Should name the horizon, inputs and evaluation method, and report error by regime, not one average. Look for honest discussion of weaknesses. Worry if they only cite a single score or cannot explain how it was validated.

  2. How do you check that prediction intervals are calibrated?

    Should cover coverage tests across regimes and horizons, reliability checks and recalibration when drift appears. Strong answers note that intervals can look fine on average and fail in storms. Worry if they treat intervals as a fixed multiple of error.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We forecast variable renewable generation and the uncertainty around it, so that operations and trading teams can make confident decisions on a constrained grid. We are looking for a scientist who can build forecasts we can measure, trust and explain, and who cares about how they are used.

What you will do

  • Build and maintain generation forecasts for wind and solar assets across several horizons
  • Produce calibrated prediction intervals and monitor their coverage
  • Set up rolling evaluation across weather regimes
  • Clean and validate telemetry, including outages and curtailment
  • Feed forecasts into simulation and optimisation systems
  • Investigate large forecast misses and share findings with operations
  • Agree with stakeholders which errors matter most

What you will bring

  • Demonstrated forecasting work on real generation or comparable time-series data
  • Evidence of calibrating and testing uncertainty estimates
  • Strong grasp of weather data and its limits as a model input
  • Experience handling noisy, incomplete telemetry
  • Solid scientific programming and reproducible workflows
  • Clear communication with operational users

Nice to have

  • Experience with grid simulators or digital twins
  • Familiarity with optimisation or operations research
  • Experience supporting live operations or incident response

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Time-series modellingAdvancedModel temporal dependencies and forecasts
  • Optimisation and operations researchAdvancedChoose actions subject to objectives and constraints
  • Simulation and digital twinsWorkingBuild useful virtual representations of environments
  • Reliability and incident responseWorkingDetect failures, restore service and prevent recurrence

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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