$175,900top of the range in California · middle $99,070 / yr
AI augments this role
Space Scientists in the United States earn a median of $99,070 a year. Pay starts near $53,060. Pay reaches $175,900 at the top of the range in California, the best-paying state for this work among those with at least 500 people in the job.
Source: U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025 (Atmospheric and Space Scientists, SOC 19-2021). Last checked 9 September 2026.
Entry level
$53,060
Top of the range · California
$175,900
Education
Doctoral degree in Physics or Aerospace
Wages — U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025 (Atmospheric and Space Scientists). Top of the range is the highest state-level figure among states with at least 500 people in the job. AI-impact rating is PayCrunch's editorial assessment. Updated September 2026.
🆕 New & Trending AI Tools for Space ScientistReviewed September 2026
We track new AI-tool launches every week and refresh this list — here’s what’s gaining traction for Space Scientist work right now.
Julius AINEWFree / $20 mo
AI data analyst that runs statistics and charts from plain-language prompts.
How a Space Scientist uses it: analyze datasets and generate figures without writing code
NotebookLMNEWFree / $7.99 mo
Google tool that answers questions grounded only in the documents you give it — with citations.
How a Space Scientist uses it: load your own manuals, policies, or PDFs and ask questions that stay accurate to the source
ElicitFree / $12 mo
AI research assistant that finds and summarizes papers.
How a Space Scientist uses it: run a literature review and extract findings across dozens of papers fast
ConsensusFree / $9 mo
AI search that answers questions from peer-reviewed research.
How a Space Scientist uses it: get evidence-backed answers with the studies behind them
SciSpaceFree / paid
AI that explains papers and helps with literature review.
How a Space Scientist uses it: decode dense papers and trace citations quickly
SciteFree / $20 mo
Shows whether other studies support or contradict a paper's claims (Smart Citations).
How a Space Scientist uses it: check if a finding is actually backed by the wider literature before you cite it
ChatGPTFree / $20 mo
The most-used AI assistant — writing, analysis, research, and images from a plain-language chat.
How a Space Scientist uses it: draft emails and documents, summarize long files, and get instant answers to on-the-job questions
ClaudeFree / $20 mo
AI assistant known for careful writing, long-document analysis, and coding.
How a Space Scientist uses it: analyze big reports or spreadsheets and turn messy notes into clean, finished writing
Google GeminiFree / $20 mo
Google's AI assistant, built into Gmail, Docs, and Search.
How a Space Scientist uses it: draft and reply inside Google Workspace and research without leaving the page
Work that starts where the everyday forecast ends
A space scientist studies the region above the weather most people feel, and the connection between that region and the Earth and the Sun. The week can be a model of the upper atmosphere, an instrument record from a satellite, a forecast of conditions that affect radio and navigation, or a paper that has to survive review. Some of this work sits inside agencies. Some sits in university groups. Some sits with contractors who build or operate the tools the agencies use. The public picture is a launch. The job is usually a dataset, a meeting, and a result you can defend.
The title covers people who work on space weather, the ionosphere, the magnetosphere, planetary atmospheres, and related observing systems. It sits beside atmospheric science, and many employers hire across that boundary. You might spend a season validating a satellite product, then a season writing the method so someone else can rerun it. You might brief operators who need a plain account of a solar event. You might teach. What ties the days together is evidence. A claim without a method will not last in this field, no matter how exciting the sky looked that week.
Models, instruments, and the review that follows
A research week rarely looks like a movie. You read what the last paper actually measured. You check whether your code still runs on the new file. You compare a model run with an observation and figure out which one moved. You sit in a review where a colleague asks the question you hoped to postpone. You write the answer down, because the next person to touch the archive will need it. If you support operations, the day adds a shift of monitoring and a briefing that has to be short enough for a decision maker without a specialist's training.
Collaboration is the other half of the job. Instruments are built by teams. Missions are proposed by teams. A scientist who cannot explain a result to an engineer, a program manager, or a student will struggle, even with strong technical skill. So will a scientist who treats every shared dataset as a private trophy. Hiring managers look for both the depth and the handoff. They want to see a figure you made, a method you can describe without hiding, and a role you played that was not only "helped."
Graduate study is the usual door
A graduate degree is the usual path into this work. A bachelor's degree in physics, atmospheric science, astronomy, geophysics, or a close field is the common start. From there, a master's degree can open applied and support roles: data analysis, instrument teams, forecasting support, and research staff positions tied to a project. A doctorate is the usual path into a role where you lead a research question, compete for funding, and publish as an independent author. Coursework matters, and the research record matters more. A thesis, a first-author paper, or a mission product with your name on the method is the proof graduate school is supposed to produce.
Choose the graduate group for the work, not for the postcard. Read what the adviser publishes. Ask where recent students went. A group that flies instruments will train you differently from a group that lives in models, and both are legitimate. Funded research assistant work is the usual way people eat while they study. Teaching assistant work is another. Either one belongs on a resume if you can say what you did. Keep your code, your notes, and your figures in a form a stranger can follow. That habit is part of the training, and it is the habit laboratories hire.
What you carry instead of a licence
No state board licences you to be a space scientist. There is no practice card that authorizes a model or a satellite analysis. The credential is the degree plus the work. Employers may also require a background check or a clearance when the data or the site demands it. That clearance is granted by the government process attached to the job. It differs from a professional licence, and you cannot claim it before the process finishes. Apply, tell the truth on the forms, and let the process run. Do not decorate a resume with a clearance level you do not hold.
Professional societies and conference talks are part of how the field recognizes you. They are not licences either. A poster, a talk, or a paper shows that peers have seen your work. A hiring committee will still read the method. If your experience is operational, bring a sample briefing with sensitive details removed and be ready to explain the decision the briefing supported. If your experience is academic, bring the paper and be ready to say what you would do differently with another year of data. The absence of a licence does not mean the bar is soft. It means the bar is the record.
Degree, record, clearance
Keep the three ideas separate. The graduate degree is the usual path into the field. The papers, products, and briefings are the proof you can do the work. A clearance, when a job requires one, is a government determination for that work. None of the three is a state licence to practice a profession.
Agencies, campuses, and the contractors between them
Federal laboratories, science agencies, universities, and aerospace contractors hire this skill. A posting may say scientist, research scientist, space weather scientist, or a specialist title tied to one instrument. Read the duty list. Some jobs are mostly research. Some are mostly operations and forecasting support. Some are mostly calibration and data quality. The title on the advertisement is less useful than the verbs. If you want a research path, a job that is only shift monitoring will frustrate you. If you want operations, a soft-money university post will feel unstable for reasons the posting may only hint at.
Applications in this field are slow and specific. Submit the degree, the publications or technical reports, and a statement that matches the posting's actual problem. Name the tools you use and the datasets you have touched. Ask a adviser or a supervisor to speak to the part of the work they saw. A generic letter about a passion for space will not separate you from the rest of the file. A paragraph about a problem you already worked, and what the result changed, will. If the role needs a clearance, say whether you have held one. If you have not, say that too. Honesty keeps the process shorter than a surprise does.
From a thesis to a staff scientist's name on the door
The early path after graduate school is often a postdoctoral appointment, a research staff role, or a contractor seat supporting a mission. You are proving you can finish without the structure of a thesis deadline. Publish or deliver the product your project promised. Learn how proposals work if you want to stay in research, because someone has to pay for the next year of computing and salary. Learn how operations hand off if you want to stay in forecasting or instrument support, because a result that arrives too late does not help the person on duty.
Later, people become staff scientists, university faculty, program scientists, or leads of a small instrument team. Some move into management of a research group. Some stay individual contributors because the science is the point. The fork is real, and neither side is a failure. What both sides share is a trail of finished work that other scientists can find. Keep that trail clean: your name used consistently, your code commented, your briefs dated. When you negotiate pay, that trail is what you are pricing. A degree opened the door. The work after the degree is why a laboratory keeps you.
A contractor seat and an agency staff seat can look identical in the first month and diverge after that. Contractors often sit next to civil servants, work on the same archive, and still have a different employer, a different review cycle, and a different way of losing the work when a contract turns over. Ask who employs you, how long the funding is expected to last, and whether authorship of a paper or a product is encouraged. Ask who speaks for you when the mission team argues about a dataset. Those answers change the job more than the word scientist on the badge. Take the seat that matches the life you want, and describe that choice plainly when the salary talk starts, so nobody confuses a short project hire with a senior research post. Bring one concrete product to that talk: a dataset you validated, a briefing operators actually used, or a paper that changed how the group treats a measurement. Say what you did versus what the team did. Scientists respect that distinction, and hiring managers use it to separate a name on a long author list from a person who can lead the next question. The degree remains the usual door. The product is why the offer should move.
A national research wage, and a high end in California
Read pay from the Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025. This paragraph is the one time to name the series: Atmospheric and Space Scientists. The title is shared with atmospheric scientists, so the dollars cover a wider scientific group than space research alone. The national middle is $99,070. The lower published figure is $53,060. The high end of the published range is $175,900 in California. From the lower figure to the national middle is $46,010. From the national middle to that high end is $76,830. Hold the series name to this explanation, then negotiate with the figures themselves.
An offer near $53,060 sits on the lower published figure, $46,010 under the national middle. That can match an early staff or support role, especially while you are still building a record beyond the degree. It is a poor match for a scientist who already leads a product, a forecast desk, or a funded question. Ask what the seat is allowed to author, whether the funding is stable, and when pay is reviewed. Do not answer that offer by quoting $175,900. The larger number is the high end of the published range in California, and it sits $76,830 above the national middle. The distance is the point. It describes a climb, not a starting salary with a coastal address attached.
An offer near $99,070 matches the national middle of that broader scientific group. Use it as the ordinary comparison for a working scientist who is past training and not yet at the top of the range. If someone waves $175,900 as what California pays scientists in this field, label it correctly: it is the high end of the published range, not a typical wage. Bring it into the conversation when the role looks like that top: a senior staff scientist, a lead on a mission product, or a specialist whose record is why the group won the work. Put the offer and $99,070 on the same line. Add $53,060 if the employer is anchoring low, and name the $46,010 gap. Add $175,900 only with its label as the California high end, and name the $76,830 gap so the figure stays in proportion. The degree got you in the room. The record, matched to these three figures, is how you talk about the job they are actually offering.
The top of Space Scientist pay — and how to get there with AI
$175,900what Space Scientist pay reaches in California
Highest state-level top-of-range annual wage for Atmospheric and Space Scientists, among states with at least 500 people in the job. U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025.
And the role it leads to — Physical Scientists, All Other — reaches $225,810 in Colorado.
$53,060entry$99,070middle$175,900top end
The best-paid people here are not the ones who can predict everything; they are the ones two or three organisations telephone when a single environment is about to cost them hardware, a launch window or a satellite pass.
Gathering data from satellites, radar and upper air stations, interpreting it against physics and computer models, and formulating predictions from environmental data is the shared craft. Post-processed guidance and shared model output have made the general version of that work cheap. What stays scarce is a person who has spent years on one narrow environment and can write a scientific report on it that operators act on. Choosing that corner early, and refusing the pull toward covering everything, is the decision this pay range actually rewards.
Your playbook, by where you are now
Just startingGet your hands on the raw feeds
Learn to pull and subset data yourself in C++ or Perl on a Linux box rather than waiting on someone to export it.
Take the shifts that involve measuring wind, temperature and humidity in the upper atmosphere with weather balloons, because instruments teach what plots hide.
Keep a Microsoft Access catalogue of every notable event you work, with what the models said beforehand and what actually happened.
Say yes to speaking with the public, because explaining an environment to a non-specialist exposes exactly where your own understanding is thin.
What proves it: Working code that ingests your own data, plus an event catalogue nobody else keeps.
Realistic span: the first two years
A few years inPick one environment and publish inside it
Choose a single narrow problem, upper-atmosphere density and drag, charging, radiation dose or signal disturbance, and stop taking work that pulls you off it.
Write scientific reports and articles on that one problem until your name is the one attached to it.
Quantify your own prediction record in IBM SPSS Statistics against the operational baseline, not against climatology.
Build one repeatable graphic in ESRI ArcInfo and one clean figure style in Adobe Photoshop, and never redesign either.
Load the specialist literature into NotebookLM to find what changed while you were on shift, then read every source you intend to cite.
What proves it: A published record on one environment plus a documented skill score for your predictions in it.
Realistic span: roughly years three to eight
ExperiencedBecome the named source
Prepare standing briefings for the operators whose decisions your environment touches, and learn their thresholds rather than describing conditions.
Take the managerial half of the job, the work schedules and staff training and matching expertise to situations, since narrow experts who can run a group are rare.
Teach the specialism, because a course is the fastest way to make a narrow reputation portable.
Look at where this work clusters, with California at the head of the state table and space, defence and instrument employers behind it.
Watch the neighbouring physical science roles, which is where a specialist usually crosses when the pay stops moving.
What proves it: A standing briefing with named operational users who ask for you personally.
Realistic span: year nine onward
The next 90 days
In the next ninety days, choose your corner and start the catalogue that will justify it. Pick one environment that costs somebody money when it misbehaves, then go back through every event of that kind in the last two seasons and write up each one the same way: what the observations showed, what the models predicted, what happened, and how far off the prediction was. Ten entries is enough to see a pattern nobody has written down. Turn the clearest one into a short scientific report and send it to the group inside your organisation that would have used it. That is how a space scientist stops being one of several forecasters and starts being the person a specific problem belongs to.
Wage figures: BLS OEWS, May 2025. The playbook is PayCrunch editorial guidance, not a guarantee of pay or placement.
Every figure is the national median from the U.S. Bureau of Labor Statistics (OEWS) shown on that role’s own page.
Never used AI before? Start here (2 minutes).
Start by putting an AI copilot on top of the Python stack you already use. Add GitHub Copilot to your IDE (or switch to Cursor) working over Astropy and SunPy, and let it write the calibration, coordinate-transform, and plotting code you retype every project. Read the images and physics yourself; let the copilot handle the boilerplate.
For staying current and learning, make NASA ADS (the Astrophysics Data System) your literature backbone, browse arXiv daily, and prototype in a free Google Colab notebook with Gemini. Keep any export-controlled or unpublished mission data out of all consumer tools.
The one rule, forever: Most launch, propulsion, and satellite technical data is export-controlled (ITAR/EAR): never paste ITAR/EAR-controlled, proprietary, or unpublished mission data into a consumer AI tool, and use only approved internal systems for controlled work. And AI cannot certify physics, so independently verify every derived quantity, unit, coordinate frame, and uncertainty before it goes into a paper or a mission decision.
The plays — exact steps, exact prompts
Do these in order. Each one is copy-paste ready. You do not need to know anything about AI going in.
1
Automate data reduction instead of hand-coding it
Why this pays: Papers are the currency of a science career, and most of the time to a paper is spent reducing raw data. An AI copilot on your Python stack cuts that sharply, so you publish more, the record that drives promotion to senior scientist.
CursorGitHub CopilotAstropy / SunPy
1
Add GitHub Copilot to your IDE or move to Cursor, working on top of Astropy, SunPy, or your instrument's pipeline. Let it write the calibration, coordinate-transform, and plotting boilerplate.
2
Describe a reduction step in plain language and have the copilot draft the Astropy code.
Copy-paste this prompt
Using Astropy, write a function that takes [a FITS file / list of FITS files] from [instrument, e.g. CCD imaging], applies [bias/dark/flat calibration], reprojects to [WCS / coordinate frame], and returns a science-ready array with an updated header. Vectorize with numpy, preserve units with astropy.units, and flag every assumption you make about the header keywords.
Check the copilot's unit and WCS handling against a known calibration; a silent unit or frame error corrupts every downstream result.
3
Refactor a one-off reduction script into a reproducible, tested pipeline with Claude Code so the whole collaboration can rerun it.
What you'll haveReduction pipelines built in days, not months, so more telescope and mission time turns into publishable results.
2
Stay at the research frontier with AI literature mining
Why this pays: The scientist who spots the open question first gets the high-impact paper. AI-assisted search over NASA ADS and arXiv keeps you ahead of a fast-moving field without drowning in PDFs.
NASA ADSElicitSciSpace
1
Use NASA ADS (the Astrophysics Data System) as your backbone for citations, and its similarity and recommendation tools to surface papers you would otherwise miss.
2
Structure a fast review with Elicit or SciSpace before you commit to an analysis angle.
Copy-paste this prompt
For the topic [e.g. coronal mass ejection arrival-time prediction], build me a structured review: the dominant methods and their reported accuracy, the key open problems, the main datasets and missions used as evidence, and the 5 must-read papers of the last 3 years. Flag where results conflict.
Verify every paper and number against NASA ADS directly; LLMs invent plausible-looking citations that do not exist.
What you'll haveA sharp read on the open problem and the right method, reached in an afternoon, the edge behind high-impact papers.
3
Script orbit and mission analysis with AI
Why this pays: Mission-facing skills (trajectory, coverage, environment analysis) pay more than pure data work, especially at space companies. AI turns you into someone who scripts these tools fast, not just clicks through them.
Ansys STKNASA GMATpoliastro
1
Drive STK or GMAT through their scripting and automation interfaces instead of the GUI, and use open-source poliastro in Python for quick trajectory work.
2
Have an AI copilot draft the automation script for a repeatable analysis.
Copy-paste this prompt
Write a [STK Connect / GMAT script / poliastro] routine that propagates a satellite from [orbital elements or TLE], computes [access windows to a ground station at lat/lon, or eclipse times, or ground-track], and exports the results to CSV. Comment each step and state the coordinate frame and time system you are assuming.
Confirm frames, epochs, and time systems (UTC vs TT/TDB, leap seconds) explicitly; a frame mistake silently ruins mission geometry. Keep controlled mission data out of consumer tools.
What you'll haveRepeatable mission-analysis scripts you can run in minutes, the mission-facing skill set that commands top-band pay.
4
Find the anomaly in big data and telemetry
Why this pays: Whether it is a transient in a survey or a fault in spacecraft telemetry, being the person who catches the signal in millions of rows is high-value, hard-to-replace work. AI copilots make building those detectors fast.
scikit-learnPyTorchGitHub Copilot
1
Prototype anomaly and transient detection with scikit-learn (isolation forests, clustering) before reaching for deep models in PyTorch.
2
Let a copilot scaffold the detector and an honest evaluation.
Copy-paste this prompt
I have [describe telemetry or survey data: columns, cadence, size]. Draft a scikit-learn pipeline to flag anomalies, with sensible preprocessing for [gaps / non-stationarity], a way to rank flagged events by severity, and an honest evaluation given that true anomalies are rare and mostly unlabeled. List the failure modes of this approach for my data.
Validate flags against known events and your physical intuition; unsupervised detectors surface instrument artifacts as readily as real signals.
What you'll haveDetectors that surface the rare, important event fast, the kind of hard problem that anchors a senior research role.
5
Win more funding with AI-assisted proposals
Why this pays: For research scientists, funded proposals are salary; they buy your time, your students, and your promotions. AI helps you write more, tighter proposals and hit every solicitation requirement.
ClaudeChatGPTNASA ADS
1
Draft and tighten the science justification with Claude or ChatGPT: you provide the ideas and results, it sharpens structure, clarity, and flow.
2
Pressure-test the proposal against the solicitation before submitting.
Copy-paste this prompt
Here is my draft science justification for a [NASA ROSES / NSF] proposal to [program]. Act as a tough review panelist: identify the weakest claims, where the methodology is under-specified, what a reviewer would attack, and what is missing versus the solicitation's stated evaluation criteria [paste criteria]. Be specific and harsh.
Never paste unpublished proprietary ideas into a consumer tool if IP or export rules apply; use approved tools and keep controlled content out.
What you'll haveMore competitive, fully compliant proposals per cycle, the funding record behind a senior scientist's salary.
6
Publish and present with AI as your drafting partner
Why this pays: Clear papers and talks build the reputation that gets you cited, invited, and promoted. AI accelerates the writing and figure-making so more of your work reaches the community.
OverleafClaudematplotlib
1
Write in Overleaf and use Claude to tighten prose, restructure sections, and draft response-to-reviewer letters, with you checking every scientific claim.
2
Generate publication-quality figures faster with AI-written matplotlib.
Copy-paste this prompt
Write matplotlib code for a publication-quality figure showing [describe, e.g. modeled vs observed flux with error bars and a residual panel]. Use a colorblind-safe palette, correct axis labels with units, and a layout that fits a two-column journal. Explain the choices I might want to change.
You own every number and axis; verify the figure against your data before it goes in the paper.
What you'll haveMore papers and sharper talks out the door, the reputation engine behind top-of-range scientist pay.
Your 12-month sequence to the top of the range
How the plays above stack into a path from median pay toward the $175,900 tier.
Month 1
Add a coding copilot to your Astropy/SunPy workflow and make NASA ADS plus Elicit your literature engine.
Months 2-3
Automate one full data-reduction pipeline and, if mission-facing, start scripting STK or GMAT instead of clicking through the GUI.
Months 3-6
Build an anomaly or analysis tool for your dataset, and draft your next proposal with AI as a review partner.
Months 6-12
Own an instrument, dataset, or subsystem end to end and lead a proposal, the senior/mission-scientist move that reaches the top band.
Gear for this job
As an Amazon Associate, PayCrunch earns from qualifying purchases. Links to books and tools are for the job on this page; we only recommend what we’d use in the work.
Same live O’Reilly 3rd already on data-scientist / python-developer / market-research-analyst / astronomer / physicist. This leftover page opens with putting an AI copilot on top of the Python stack you already use over Astropy and SunPy; Month 1 is Add a coding copilot to your Astropy/SunPy workflow. Not CompTIA Data+ and not leftover Ross Exam P (that is actuary). Confirm 109810403X. Live page HTTP 200, no PC_GEAR / amazon.com/dp / tag=paycrunch-20 at 2026-09-17 4:36 PM PT.
Next steps for a Space Scientist
Some links below are affiliate or partner links. PayCrunch may earn a commission if you enroll or subscribe through them, at no extra cost to you. Wage figures on this page still come from the Bureau of Labor Statistics, not from these programs.
Space Scientist work is specific enough that a stamped 'check out these courses' block would be noise. BLS files this work as Atmospheric and Space Scientists (SOC 19-2021). O*NET Job Zone 4 is typical: a bachelor's degree, so the honest next credential is a professional certificate or bachelor's-level coursework — not a random catalog dump.
The occupation's listed knowledge areas include Physics and Geography; the links search those subjects, not a generic 'career courses' list.
Space Scientists in this dataset list C++ among the tools in use, so a program that names that stack is a better fit than a survey course.
Coursera search for physics — a professional certificate or bachelor's-level coursework that lines up with science, not a generic professional-development aisle.
FlexJobs screens remote, hybrid, freelance, and flexible listings so you are not wading through unverified ads. This is a job-board search for Space Scientist work, not a claim that they list a counted SOC 19-2021 inventory.
Write a Space Scientist resume, or one aimed at Physical Scientists, All Other, instead of a blank template. Resume Now is a resume builder; we are not claiming a counted template set for this SOC.
A Space Scientist resume that names the actual tasks on this page, or the step-up title Physical Scientists, All Other, beats a blank template when you apply.
What Space Scientists earn by state
This page does not show a state table, and the reason is worth stating: the Bureau publishes this occupation nationally, but fewer than five states employ enough people in it to report a median we would stand behind. Scaling the national median by a cost-of-living index would produce a number for every state, but it would be an estimate of living costs wearing a wage’s clothes, and PayCrunch would rather show you nothing than that.
What the national figures say: pay starts near $53,060, the median is $99,070, and the top of the range is $175,900. Those national figures come from U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025.
No. Physics judgment, mission responsibility, instrument expertise, and the ability to know when a result is physically impossible stay human. AI is augmentation: the scientist who uses it reduces data and writes proposals faster; the one who ignores it falls behind on output.
Can I use ChatGPT or Claude with mission data?
Not with ITAR/EAR-controlled, proprietary, or unpublished mission data. That work belongs in approved internal systems only. Use consumer tools for published science, general methods, and learning, phrased so nothing controlled leaves the building.
Will AI-written analysis code introduce errors?
Yes, especially in units, coordinate frames, time systems, and calibration, where a silent mistake looks like a real result. Re-run generated code against a known calibration and check the physics before you trust the output.
Do I still need deep physics if AI can write the code?
Yes, more than ever. Deep physics is what lets you catch the wrong method, the missing systematic, or the impossible number the AI produced with total confidence. Judgment is the part that does not get automated.
How does this actually raise my pay?
More publications and funded proposals move you up the research ladder, and mission-facing scripting skills (STK, GMAT, telemetry analysis) open the higher-paying space-industry roles that make up the top of the band.
Methodology & sources
Salary (median, 10th, top of the range) — U.S. Bureau of Labor Statistics, OEWS.
By state — the Bureau of Labor Statistics’ own state medians, limited to states employing at least 500 people in the occupation. No cost-of-living arithmetic is applied to a wage anywhere on this page.
The plays — PayCrunch's own step-by-step guidance using publicly available AI tools. Tool names/URLs are real and current as of August 2026; prompts written to work as-is. Verify any professional output before relying on it.