Interview scorecard template

Remote Sensing Data Analyst interview scorecard

Evaluate Remote Sensing Data Analyst candidates across 4 weighted areas: technical proficiency, systems and trade-offs, evidence and rigour, and collaboration and communication. Technical proficiency leads at 35%, so check hands-on command of Python (rasterio, GDAL, xarray), Google Earth Engine or ENVI, and specific sensors: Sentinel-1 SAR, Sentinel-2, Landsat, PlanetScope, MODIS. Use the rubric to compare role-specific evidence consistently.

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software datageospatial analysisgoogle earth engineremote sensingsar imagery
TL;DR
For technical proficiency, look for evidence the candidate names sensors, bands and resolutions from memory, and describes atmospheric correction, orthorectification and speckle filtering they ran themselves. For systems and trade-offs, look for evidence the candidate explains a concrete trade-off, for example coarser temporal compositing to cut cloud gaps, with runtime and storage numbers attached. Apply the written 1–5 anchors to every answer, record the evidence behind each rating, and use the factor weights to reach a consistent overall assessment.
Complete evaluation framework

What to assess and how to score it

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

Technical proficiency

35% weight

Check hands-on command of Python (rasterio, GDAL, xarray), Google Earth Engine or ENVI, and specific sensors: Sentinel-1 SAR, Sentinel-2, Landsat, PlanetScope, MODIS.

Evidence to listen for

  • Command of the languages, frameworks, and data tools the role actually uses
  • Understands correctness, performance, and failure modes, not just syntax
  • Has opinions on testing and can justify them
  • Reads and reasons about code they did not write

Five-point scoring guide

1
Poor

Cannot work independently; fundamentals are missing.

2
Needs Improvement

Weak fundamentals; output needs heavy review.

3
Satisfactory

Competent for the role; needs guidance on complex or unfamiliar work.

4
Very Good

Strong practitioner; handles hard problems with little guidance.

5
Excellent

Names sensors, bands and resolutions from memory, and describes atmospheric correction, orthorectification and speckle filtering they ran themselves.

02
Evaluation factor

Systems and trade-offs

25% weight

Probe how they handle petabyte-scale archives: cloud masking strategy, tiling and mosaicking choices, COG versus NetCDF storage, and compute cost on AWS or GEE quotas.

Evidence to listen for

  • Reasons about scale, latency, cost, and failure before writing code
  • Names the trade-off they chose and what they gave up
  • Understands the data lifecycle end to end
  • Anticipates what breaks at ten times the volume

Five-point scoring guide

1
Poor

No thinking beyond the immediate task; no awareness of scale or failure.

2
Needs Improvement

Limited architectural awareness; struggles with design decisions.

3
Satisfactory

Works within an existing design; makes sound local decisions.

4
Very Good

Designs for scale and maintainability; articulates trade-offs clearly.

5
Excellent

Explains a concrete trade-off, for example coarser temporal compositing to cut cloud gaps, with runtime and storage numbers attached.

03
Evaluation factor

Evidence and rigour

25% weight

Test validation habits: ground truth collection, confusion matrices, kappa or F1 per class, spectral separability checks, and how they quantified uncertainty in change detection outputs.

Evidence to listen for

  • Validates results rather than trusting output
  • Knows how their work is measured and what a bad result looks like
  • Can describe a time their own analysis or model was wrong and how they caught it
  • Careful about data quality, leakage, and silent failure

Five-point scoring guide

1
Poor

Ships unvalidated work; no notion of how correctness is checked.

2
Needs Improvement

Validates superficially; misses obvious quality or leakage issues.

3
Satisfactory

Reasonable checks in place; rigour drops under time pressure.

4
Very Good

Validates thoroughly; can name a real error they caught in their own work.

5
Excellent

Quotes per-class accuracy from a real classification, admits where the model failed (for instance shadowed slopes), and shows the fix.

04
Evaluation factor

Collaboration and communication

15% weight

Assess how they deliver maps and time series to non-specialists: agronomists, planners, insurers; look for QGIS layouts, dashboards, and clear caveats on pixel-level claims.

Evidence to listen for

  • Explains technical work to non-technical stakeholders
  • Gives and takes code or peer review constructively
  • Documents enough that the work survives their absence
  • Aligns with team process rather than working around it

Five-point scoring guide

1
Poor

Cannot work in a team; resistant to feedback.

2
Needs Improvement

Communication issues create rework; lone-wolf tendencies.

3
Satisfactory

Adequate team member; documentation and review participation are light.

4
Very Good

Communicates well; reliable reviewer and collaborator.

5
Excellent

Describes translating a raster product into a decision a client acted on, while stating resolution limits without overselling the imagery.

Evidence-led prompts

Interview questions for a Remote Sensing Data Analyst

Use these prompts to surface evidence for the weighted factors above and compare candidates against the same role-specific criteria.

  1. 01

    Can you describe your experience with satellite imagery analysis?

  2. 02

    Describe a challenging remote sensing project and how you handled the difficulties.

  3. 03

    Have you worked with uncrewed aerial imagery, and in what capacity?

  4. 04

    How do you approach preprocessing of remote sensing data before analysis?

  5. 05

    What steps do you take to ensure the accuracy and quality of the data?

See the complete Remote Sensing Data Analyst question set
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