Why pre-screen satellite data analysts before the technical interview
An image always produces an answer, and the answer is often about the atmosphere rather than the ground. Cloud, haze, illumination angle and pixel size all change what is measurable, and a classification can look convincing while being driven by acquisition conditions. Analysts worth hiring say what the data cannot support. A short screen asks what the imagery could not tell them on a real project.
What actually matters when screening Satellite Data Analyst candidates
- 01
Method and rigour
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.
- 02
Real casework
Probe actual imagery projects: which missions (Sentinel-1/2, Landsat 8/9, PlanetScope, MODIS), area covered, time series length, and who consumed the deliverable.
- 03
Interpretation and judgement
Assess how they separate real change from artefacts: seasonal phenology, sensor drift, terrain shadow, mixed pixels, and disagreement between optical and radar signals.
- 04
Reporting and testimony
Look for map and report craft: cartographic standards, metadata and CRS documentation, reproducible Earth Engine or Python notebooks, and briefings to non-geospatial clients.
Pre-screening questions to ask Satellite Data Analyst candidates
12 questions grouped by what they test. Ask the same set in every screen and score answers on a consistent scale, or send them as an async video screen and compare answers side by side.
Analyses that were used
3 questions01Can you explain a complex analysis you conducted on satellite data?
Listen forA specific analysis with the method described, and what the result was actually used to decide.
Analyses described by tooling, or work that produced a map nobody used for anything.
02Have you worked on a project using satellite data for predictive modelling?
Listen forA model with predictions tested against later observations, and its accuracy stated honestly.
Predictions never checked against outcomes, or accuracy quoted from training data only.
03Can you describe creating a data-driven solution that solved a real problem?
Listen forA problem owned by someone else, with the analysis shaped by what they needed to decide.
Solutions built for their own interest, or no user for the output beyond the analyst.
Knows sensor limits
3 questions04What is your experience with satellite imagery interpretation?
Listen forInterpretation grounded in spectral behaviour, with seasonal and illumination effects accounted for.
Interpretation by visual appearance alone, or acquisition conditions never considered.
05Do you have experience working with multi-spectral or hyper-spectral data?
Listen forBand selection justified by what is being measured, with atmospheric correction applied properly.
Bands combined by convention, or atmospheric correction skipped on multi-date comparisons.
06Do you have experience dealing with remote sensing data?
Listen forMultiple sensors used with revisit time, resolution and cloud cover treated as real constraints.
One data source used regardless of the question, or cloud cover ignored in a time series.
Processing in code
3 questions07Tell us about your programming experience, particularly in Python or R.
Listen forAnalysis written as code that can be rerun, with the geospatial libraries used named specifically.
Work done entirely through a graphical interface, or steps that cannot be repeated exactly.
08Can you describe your experience applying machine learning to satellite data?
Listen forTraining data collected properly, with spatial separation between training and test areas.
Training and test samples drawn from the same locations, or accuracy inflated by spatial overlap.
09What is your understanding of cloud platforms for processing imagery at scale?
Listen forLarge archives processed without downloading everything, with compute cost understood and tracked.
Every scene downloaded locally, or processing cost never considered on a large archive.
Validated on the ground
3 questions10Have you performed error checking and data cleaning on large datasets?
Listen forSystematic checks for missing scenes, sensor artefacts and geolocation errors before analysis.
Data taken as delivered, or artefacts discovered only when results looked strange.
11Are you familiar with data visualisation and producing detailed reports?
Listen forOutputs that state accuracy and limitations, with legends and scales that a non-specialist can read.
Maps published without accuracy figures, or confidence in results overstated in the write-up.
12Have you used geographic information system software, and which packages?
Listen forWorking knowledge of the standard tools, with coordinate systems and reprojection handled carefully.
Software listed without depth, or projection handling treated as an automatic step.
How to score responses
Score every candidate on the same four criteria immediately after the screen. At this stage you are shortlisting for panel interviews, not making the final call.
Method and rigour
35%5Names exact preprocessing chains per sensor, defends resampling and correction choices, and reports classification accuracy with independent ground truth.
Real casework
25%5Describes specific analyses (crop mapping, deforestation alerts, flood extents) with scene counts, revisit constraints, and the decision each product informed.
Interpretation and judgement
25%5Distinguishes physical change from processing artefact using independent evidence, states confidence bounds, and refuses conclusions the resolution cannot support.
Reporting and testimony
15%5Produces clear map products with legible legends and uncertainty layers, and explains limitations to policy or operations audiences without jargon.
An image always produces an answer, sometimes about the atmosphere rather than the ground. A one-way video screen asks for the limits.
Try it on HirevireScreening FAQ
Process basics
How long should a pre-screening round for this role take?
Fifteen minutes across eight to ten questions, answered async. Enough to establish analyses they delivered, test their sensor and processing knowledge, and check how they validate results.
How much coding should I expect?
Enough to process imagery at volume and rerun it later. An analyst working only through a graphical interface will struggle with time series and cannot reproduce their own results.
Evaluating answers
What is the strongest signal when screening this role?
What the imagery could not tell them. Analysts with real experience name the limits of resolution and conditions. Anyone whose imagery answered every question has not checked the answers.
How do I judge their validation?
Ask how a classification was checked. Real answers involve independent reference data and an accuracy figure. Anyone validating by visual inspection has confirmed their own expectations.
Go deeper on this role
Sanat has been hiring since 2012 and watching the recruitment industry change up close ever since, and turned that screening process into Hirevire's video screening platform. LinkedIn
