Hiring nowAgriculture, food and conservation intelligence

Conservation AI Scientist

Also called Biodiversity AI Scientist

A Conservation AI Scientist turns field observations of species and habitats into models that conservation teams can trust and act on. They matter because a model that ignores sampling bias leads to confident, wrong decisions.

What they do

Takes camera trap images, acoustic recordings, satellite imagery and survey records and builds models that detect species, track habitat change and estimate abundance. Spends much of the time on the data itself: where sensors were placed, what they missed, and how that skews results. Trains and tests image models, checks outputs against field verification, and works with ecologists, rangers and local communities to decide what the model should and should not be used for. Writes up methods and limits in plain language.

What a strong hire delivers

Within the first months, a strong hire delivers a working conservation model for one clear question, with documented field-verification records behind it. They map the main sampling biases in the existing data and say how they affect results. They agree with ecologists and local partners how detections will be checked and used. They leave a repeatable workflow that the team can run without them.

How you can tell they are good

Ask them to walk through one model they built from raw observations to a decision someone made. Listen for how they found and handled sampling bias, how they verified detections in the field, and what they told users not to trust. Strong people name failures easily. Be wary of candidates who quote accuracy scores without explaining what the data failed to represent.

Why it is hard to fill

The title is rare and the work sits between ecology and machine learning. Most ecologists have modest modelling depth in computer vision, and most ML engineers have never designed a survey or worried about detection probability. Candidates often carry titles like ecologist, remote sensing analyst or data scientist. Fieldwork credibility matters too, so the shortlist is small and needs careful reading of past projects rather than job titles.

Where to find them

Look in conservation research groups, environmental consultancies, wildlife monitoring organisations and university ecology or geography departments. Many come from titles such as quantitative ecologist, remote sensing scientist, geospatial analyst or applied ML researcher. Some arrive from agronomy or food systems work with a similar mix of fieldwork and modelling. Agtech and climate data companies are another source of people with both skills.

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 Conservation AI Scientist

  1. Describe a model you built from field observations. What decision did it inform, and how did you verify it on the ground?

    A strong answer names a real decision, the verification method and what changed as a result. Look for honest discussion of errors found in the field. Worry if verification was skipped or only done on the training data.

  2. How would you detect and correct sampling bias in camera trap data where sensors were placed near roads?

    Should cover placement effects, detection probability, covariates, stratified checks and occupancy-style modelling or reweighting. They should say what cannot be corrected. Worry if they treat more data as the fix.

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

Job description you can post

We build tools that help conservation teams understand species and habitats from field data. We are looking for a Conservation AI Scientist to turn sensor, imagery and survey observations into models that ecologists and local partners can verify and use to make decisions.

What you will do

  • Build and test models that detect species and track habitat change from images, audio and satellite data
  • Analyse sampling bias and document how it affects results
  • Design field-verification checks and keep clear verification records
  • Work with ecologists, rangers and local communities to shape how outputs are used
  • Report model limits and uncertainty in plain language
  • Maintain repeatable data and modelling workflows

What you will bring

  • Shipped or published a model built on real ecological or environmental observations
  • Demonstrated ecological modelling and sampling bias analysis
  • Hands-on experience with computer vision on field imagery
  • Experience with geospatial data and tools
  • Evidence of validating model outputs against field data
  • Ability to work respectfully with community and field partners

Nice to have

  • Experience with acoustic or camera trap datasets
  • Background in agronomy, remote sensing or food systems
  • Fieldwork experience in conservation or ecological survey

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
  • Computer visionAdvancedAnalyse or generate visual information
  • Scientific domain expertiseWorkingApply disciplinary theory and experimental standards
  • Worker and community participationWorkingInclude affected workers and communities in decisions

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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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