Hiring nowClimate, weather and Earth intelligence

AI Weather Scientist

Also called Weather ML Scientist

An AI Weather Scientist builds and tests data-driven forecast models and proves they are right when it counts. They give a company forecasts it can trust, with evidence of where those forecasts hold up and where they do not.

What they do

They train or adapt data-driven forecast models using reanalysis, observations and satellite data. They design verification: choosing metrics, baselines and test periods that reflect real use. They break results down by weather regime, season and region, and look hard at extremes such as heavy rain, heatwaves and storms. They compare against physics-based systems and judge honestly where the model is better or worse. They check that probabilities are calibrated, trace failures back to data or model choices, and explain findings to engineers, customers and non-specialists.

What a strong hire delivers

In the first months, a strong hire delivers a verification framework the team trusts and reuses. They produce a baseline forecast model or a clear evaluation of an existing one, with skill and calibration reported across regimes and extremes. They name the known weaknesses and rank fixes by value. They leave the team with cleaner data pipelines, agreed metrics and a shared view of what good looks like.

How you can tell they are good

Ask them to walk through a forecast they built or assessed and show how they verified it. Strong people talk about baselines, regimes, extremes and calibration before they mention headline scores. They can explain a failure they found and what they did about it. They know why a smooth, low-error forecast can still be useless for a rare event. Be wary of one averaged number.

Why it is hard to fill

The title is new, so people hold it under many names. Meteorologists often have limited machine learning depth. Machine learning engineers often miss verification discipline and treat weather as generic time series. Candidates strong in both are scarce, and they are usually settled in research groups or specialist teams. We screen for verification judgement first, then check modelling skill, because the first is harder to teach.

Where to find them

Look in national and regional forecasting centres, university atmospheric science and climate groups, and frontier lab or big tech weather research teams. Others come from climate analytics companies, renewable energy forecasting, insurance and catastrophe modelling, and agriculture or aviation services. Typical titles include research scientist, meteorologist, climate data scientist, applied scientist and scientific ML engineer. Earth observation and geospatial teams are a good source too.

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 an AI Weather Scientist

  1. Tell me about a forecast model you verified. Which metrics and baselines did you choose, and why?

    A strong answer names sensible baselines such as climatology or persistence, matches metrics to the use case, and explains trade-offs. Worry if they cite one aggregate score or cannot say why they chose their test period.

  2. How would you judge whether a model handles extreme events well when they are rare in the data?

    Good answers cover event-based verification, sampling limits, targeted case studies, tail behaviour and uncertainty on the estimates. Worry if they rely on overall error or assume more data fixes it.

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

Job description you can post

We are building data-driven weather forecasts that people can rely on for real decisions. We are looking for an AI Weather Scientist to develop and evaluate forecast models and to own how we prove they work, especially in difficult conditions and extreme events.

What you will do

  • Develop and train data-driven forecast models using reanalysis, observation and satellite data
  • Design and run meteorological verification across regimes, seasons and regions
  • Assess skill and calibration, with close attention to extremes
  • Compare our models with physics-based systems and report findings honestly
  • Investigate failures and recommend changes to data or modelling
  • Communicate results and uncertainty to engineers and customers

What you will bring

  • Demonstrated work building or verifying weather or climate forecasts
  • Strong understanding of verification methods and probabilistic forecast calibration
  • Practical experience with time-series or spatial machine learning
  • Comfort with gridded geospatial and Earth observation data
  • Clear written and spoken communication of uncertainty
  • Evidence of sound judgement when results are mixed

Nice to have

  • Experience with ensemble or probabilistic forecasting
  • Familiarity with operational forecasting workflows
  • Experience working with downstream users of forecasts

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

Skills that matter

  • Geospatial and earth systemsAdvancedAnalyse spatial, temporal and environmental information
  • Time-series modellingAdvancedModel temporal dependencies and forecasts
  • Scientific domain expertiseWorkingApply disciplinary theory and experimental standards
  • Uncertainty quantificationWorkingEstimate confidence, calibration and model uncertainty

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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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