26 tasks, each one witnessed by the sources that watched the job — and behind every one, a prompt you can use tonight.
You split time between desk work and hands-on science. Mornings often mean checking instruments, running experiments, or collecting samples (rock, soil, or simulated planetary materials).
Afternoons are for data analysis in Linux or Microsoft Excel, writing reports in Microsoft Word, making slides in PowerPoint, and planning field or lab work. Expect meetings, grant writing, and occasional conference prep.
Common systems include Linux for modeling and data processing, Microsoft Excel and Access for tabular data, and ESRI ArcGIS for mapping planetary terrains. IBM SPSS Statistics and C++ are used for advanced analyses and custom code.
You’ll also use PowerPoint and Word for reports, and sometimes Adobe Photoshop for figures. Lab instruments and field gear are daily tools too.
Use AI as a tool for pattern finding, image classification, or anomaly detection, not as the final answer. Always validate AI outputs against raw data and known physical models, and keep code and training data documented.
Be careful with black-box models: record versions, test on withheld data, and have human review before publishing or using results in proposals or operations.
The Bureau of Labor Statistics reports 55,850 employed planetary scientists and a median annual wage of $98,920. The lowest tenth earned $60,430, and the top tenth made $168,010. (Source: BLS 2025.)
Pay varies by employer: university, government (NASA, USGS), or industry, and by years of experience and grant success.
Planetary scientists study other planets and their processes; they combine geology (rocks, surfaces), atmospheric science, and sometimes biology (astrobiology). A geologist focuses mainly on Earth’s rocks and processes; an astronomer focuses on stars and large-scale space phenomena.
You’ll use field sampling like a geologist, and telescope/remote-sensing data like an astronomer. The job mixes lab experiments, mapping with ArcGIS, and data coding in Linux or C++.
Proficiency with data analysis on Linux plus Excel and basic C++ or scripting. That combo lets you process instrument outputs, run models, and prepare figures and tables for reports.
Teams expect you to compile and analyze measurements, keep detailed records, and produce PowerPoint or Word deliverables for grants and conferences.