Satellite Data Quant Interview Guide
Satellite data quant interview guide covering imagery, coverage, labels, timing, bias, examples, costs, and caveats.
Candidates discussing geospatial signals, alternative data, and validation.
Satellite data is geospatial evidence
Satellite imagery can proxy activity such as traffic, construction, shipping, agriculture, or inventory, but only after careful mapping and validation.
Coverage and timing matter
Clouds, revisit frequency, image quality, geography, vendor processing, and publication lag can all affect signal usefulness.
Concrete example
Parking-lot counts might proxy store traffic, but the signal needs seasonal controls, location mapping, and checks against known outcomes.
Model output is not ground truth
Computer-vision labels can be noisy, biased, or inconsistent across time, so validation against independent observations is important.
Common mistakes
Candidates often overstate the data. Satellite features are hypotheses that need timing, coverage, and economic validation.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.