Review the evidence signals before interviewing. Then use the anchored descriptions—not instinct alone—to choose the score that best matches each answer.
01
Evaluation factor
Method and rigour
35% weight
Check how they process imagery end to end: atmospheric correction (Sen2Cor, LaSRC), orthorectification, cloud masking, SAR speckle filtering, and validation using confusion matrices and kappa statistics.
Evidence to listen for
Follows and can justify an established methodology
Understands contamination, bias, and chain of custody as they apply to the field
Knows the limits of their techniques and says so
Documents procedure so results are reproducible and defensible
Five-point scoring guide
1
Poor
Careless method; unaware of contamination, bias, or procedural integrity.
2
Needs Improvement
Knows procedures but applies them inconsistently; gaps in documentation.
3
Satisfactory
Sound standard practice; less certain outside familiar techniques.
4
Very Good
Rigorous and well documented; understands the limits of each method.
5
Excellent
Names exact preprocessing chains per sensor, defends resampling and correction choices, and reports classification accuracy with independent ground truth.
02
Evaluation factor
Real casework
25% weight
Probe actual imagery projects: which missions (Sentinel-1/2, Landsat 8/9, PlanetScope, MODIS), area covered, time series length, and who consumed the deliverable.
Evidence to listen for
Brings specific cases, sites, or projects rather than general description
States their own role and what they personally handled
Can describe an ambiguous or degraded case and how they proceeded
Knows what happened to the work afterwards
Five-point scoring guide
1
Poor
No hands-on casework; experience is entirely academic.
2
Needs Improvement
Limited exposure; cannot describe their own contribution clearly.
3
Satisfactory
Real casework with adequate detail; ownership sometimes vague.
4
Very Good
Specific cases with clear personal scope and outcomes.
5
Excellent
Describes specific analyses (crop mapping, deforestation alerts, flood extents) with scene counts, revisit constraints, and the decision each product informed.
03
Evaluation factor
Interpretation and judgement
25% weight
Assess how they separate real change from artefacts: seasonal phenology, sensor drift, terrain shadow, mixed pixels, and disagreement between optical and radar signals.
Evidence to listen for
Separates what the evidence shows from what they infer
States confidence levels and what would change their conclusion
Comfortable saying the result is inconclusive
Handles pressure to reach a preferred conclusion without bending
Five-point scoring guide
1
Poor
Overstates findings; no separation of evidence from inference.
2
Needs Improvement
Reaches conclusions the evidence does not support; uneasy with uncertainty.
3
Satisfactory
Reasonable judgement; qualifies findings when prompted.
4
Very Good
Clearly separates evidence from inference and states confidence unprompted.
5
Excellent
Distinguishes physical change from processing artefact using independent evidence, states confidence bounds, and refuses conclusions the resolution cannot support.
04
Evaluation factor
Reporting and testimony
15% weight
Look for map and report craft: cartographic standards, metadata and CRS documentation, reproducible Earth Engine or Python notebooks, and briefings to non-geospatial clients.
Evidence to listen for
Writes findings that a non-specialist can act on
Has presented or defended work to an external audience: court, client, review board, publication
Withstands challenge without overclaiming or retreating
Keeps records that hold up to scrutiny
Five-point scoring guide
1
Poor
Cannot communicate findings; records would not withstand review.
2
Needs Improvement
Reporting is unclear or incomplete; avoids external scrutiny.
3
Satisfactory
Adequate reports; limited experience defending work externally.
4
Very Good
Clear reporting and real experience presenting to an external audience.
5
Excellent
Produces clear map products with legible legends and uncertainty layers, and explains limitations to policy or operations audiences without jargon.
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Score every candidate against the same standard
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