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Where an oceanographer earns the top of the range

$287,180top of the range in Texas · middle $101,920 / yr
AI augments this role

Oceanographers in the United States earn a median of $101,920 a year. Pay starts near $59,330. Pay reaches $287,180 at the top of the range in Texas, 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 (Geoscientists, Except Hydrologists and Geographers, SOC 19-2042). Last checked 9 September 2026.

Entry level
$59,330
Top of the range · Texas
$287,180
Education
Master's or Doctoral degree
Lower disruption Higher exposure AI augments this role
Entry · $59,330 Top of range · $287,180 (Texas) Middle $101,920

Wages — U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025 (Geoscientists, Except Hydrologists and Geographers). 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 OceanographerReviewed September 2026

We track new AI-tool launches every week and refresh this list — here’s what’s gaining traction for Oceanographer work right now.

Julius AINEWFree / $20 mo

AI data analyst that runs statistics and charts from plain-language prompts.

How an Oceanographer 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 an Oceanographer 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 an Oceanographer 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 an Oceanographer uses it: get evidence-backed answers with the studies behind them

SciSpaceFree / paid

AI that explains papers and helps with literature review.

How an Oceanographer 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 an Oceanographer 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 an Oceanographer 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 an Oceanographer 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 an Oceanographer uses it: draft and reply inside Google Workspace and research without leaving the page

An oceanographer can spend the morning arguing with a column of temperatures and the afternoon explaining, to a room of non-scientists, why a coastline is moving. The subject is the ocean: its motion, its chemistry, the life in it, and the seafloor under it. The workplace is often a lab, a university office, or a government building within smell of a harbor. Some years include time on a ship. Many years do not. People who arrive expecting a permanent life at sea misunderstand the career. People who arrive expecting only a quiet spreadsheet misunderstand it too.

The occupation sits inside the earth sciences and reaches into physics, chemistry, and biology depending on the project. Employers hire oceanographers to answer practical problems as well as theoretical ones. Will this harbor silt in. How is a current shifting. What is happening to a fishery habitat. What did a spill do to a bay. The daily craft is measurement already in hand, computation, writing, and the patience to say what the evidence cannot yet support. Glamour is occasional. Clarity is the job.

Motion, seawater, living systems, and the coast

Physical oceanographers study how water moves. Currents, waves, tides, the exchange of heat, and the way the ocean participates in climate are their territory. A workday might be a model run, a comparison of that model with observations a colleague collected, and a paragraph that says where the model fails. Chemical oceanographers follow what is dissolved or suspended: nutrients, carbon, trace substances, the markers of a pollution event. Their days lean on lab results and on the discipline of not overclaiming a single sample. Biological oceanographers and marine ecologists look at plankton, fish habitat, and the systems that connect them. Geological oceanographers study sediments and the shape of the seafloor, which matters to cables, to hazards, and to the history recorded in mud.

Coastal work is where many of these threads meet the public. A state agency wants to know how a beach nourishment project fared. A city wants a plainer account of flood risk. A company wants a survey before it builds. The oceanographer translates specialized results into a memo a harbor master or a planner can use. That translation is a skill, and it is undervalued by people who think the only real work happens offshore. Field days still exist: small boats, piers, marshes, and, for some projects, a research ship. Those days are part of certain jobs. They are not a script, and they are not a requirement of every posting with the word ocean in it. What must not be skipped, on any path, is the chain from observation to a conclusion someone else can check.

Writing holds the career together. Proposals, cruise-free lab reports, journal articles, and technical memos are how results survive the person who produced them. Data have to be organized so a stranger can rerun the figure. Talks at a seminar or a public meeting are part of the same duty. Oceanographers who can only speak to other oceanographers limit their employers. Oceanographers who smooth away uncertainty to please a client create a different kind of trouble. The professional habit is a clear claim, a clear limit on that claim, and the file that backs both.

The degrees a lab or an agency will actually read

There is no single national licence that creates an oceanographer. The path is educational, and the level of degree tracks the level of independence employers expect. A bachelor's in oceanography, marine science, geology, physics, chemistry, biology, or a closely related field can open technician and analyst roles: processing data, supporting a lab, helping on a field project under someone else's design. A master's is a common requirement for applied scientist jobs in government and consulting. A doctorate is the usual requirement for a university faculty post and for many research-scientist jobs where you are expected to define the project, not only carry it out.

Coursework that keeps paying off is unglamorous and specific. Physics and calculus for the physical side. Chemistry for the chemical side. Statistics and a programming language almost everywhere, because modern oceanography drowns people who cannot handle data. Geology for anyone who will talk about sediments or the coast. Writing, again. Graduate school adds a thesis or dissertation that proves you can finish a project, not only start one. Choose an advisor whose students finish and whose topics you can stand to inhabit for years. A famous name who never meets with students is a poor trade.

Field experience belongs on the resume when you have it, described as participation in a project rather than as a tally of adventures. Say what you measured, what you computed, and what you wrote. Internships with NOAA, a university lab, a state coastal program, or a survey firm teach the pace of applied work, which is different from the pace of a thesis. If your target is a licence some states require for public practice of geology, check that board separately when the job is coastal geology offered to the public. Do not assume an oceanography degree silently includes it. Do not assume every ocean job needs it.

Degree and evidence

The diploma names the training. The thesis, the code, the memo, or the paper is the evidence you can finish. Hiring groups trust the evidence. They have seen too many transcripts that never turned into a result.

Agencies, campuses, and companies that fund the work

Universities employ oceanographers as faculty, researchers, and postdoctoral scientists. The life is a mix of projects, proposals, teaching, and students. Pay and stability vary with grants, which means a brilliant project can still be a precarious job. Federal and state agencies are the other great home: ocean and atmospheric service, geological and coastal surveys, fisheries, environmental protection, and state coastal programs. The work is more likely to serve a public decision and a public timeline. Consulting firms and offshore-energy companies hire applied scientists to support permits, surveys, and environmental reviews. Those roles can pay well and can also press you to deliver on a client's calendar. Know which pressure you want before you accept.

A smaller set of jobs lives in nonprofits, museums, and teaching colleges, where explanation is half the work. Another set lives in instrument and software firms that build the tools oceanographers use. Crossing between these worlds is common and sometimes healthy. A government scientist who has never written a paper may struggle in a lab that lives on publications. A new doctorate who has never met a deadline set by a harbor project may struggle in consulting. Neither background is false. They are different fluencies, and a hiring group can hear which one you have within ten sentences.

What a hiring group looks for in the file

Start with the degree they posted, then show a finished piece of work. A thesis chapter, a technical report, a figure you made and can explain without notes, or code that turned a messy file into a result. Be ready to say what you would not conclude from that result. That sentence separates scientists from enthusiasts. If the job includes field work, describe the conditions you have actually handled and the role you held, whether you led or assisted. If the job is mostly computation, bring a concrete example and skip the ship stories. Mismatch is obvious and wastes everyone's afternoon.

Collaboration stories matter more than candidates expect. Ocean projects are rarely solo. Talk about a disagreement over a method, a dataset you inherited in bad shape, or a coauthor who needed the writing to change. Talk about a public or a client audience and how you changed your language without changing the result. Ask the employer who funds the work, how long the funding lasts, whether you will write proposals, and what "field work" means in this specific group. A posting that says oceanographer can mean a desk, a marsh, or a ship. You are allowed to want only one of those. Say so before you move across the country for the wrong Tuesday.

Technician years, a research seat, or applied practice

Early careers often look like support with increasing ownership. You process data, maintain a piece of the analysis, draft sections of a report, and learn how your supervisor thinks about error. Use that time to become the person who can be trusted with the messy middle of a project. Publishing, if you are on an academic path, should start before you feel ready, with a mentor who will actually edit. On an applied path, a report that a client used is the equivalent artifact. Keep both kinds of documents. Careers bend, and a file that shows only one dialect of the work is harder to bend later.

Mid-career splits. Some oceanographers become the scientist of record on a topic and spend years going deeper, advising students or junior staff, and representing the work in public. Some become project managers who still understand the science and now also understand budgets and schedules. Some move toward policy, translating coastal science for agencies that write rules. Some leave research for industry roles tied to energy, navigation, or environmental review. A few teach full time. The doctorate is not a vow to stay in a university, and a master's is not a vow to stay in consulting. What ages poorly is contempt for the path you did not take. The ocean problems are large enough to need all of these seats.

Geography follows the water and the institutions. Coasts concentrate jobs, and so do certain universities and federal labs far from the beach if the work is modeling or archives. A move for a two-year post can be rational. A move that ignores a partner's career and the funding cliff is how people become bitter specialists. Look at the length of the money, the mentor, and whether you will still be learning. Tools change. Satellites, autonomous vehicles, and larger datasets keep rewriting the practical skills. The underlying demand, honest inference about the ocean, does not get replaced by the tool.

Texas as a high end, and Texas again as a median

Wage figures for this career are Occupational Employment and Wage Statistics, May 2025. The table's title is Geoscientists, Except Hydrologists and Geographers, a wider earth-science series than the ocean-focused job alone, so every dollar below stays tied to the label it actually has. Entry pay is $59,330. The national median is $101,920. From entry to the national median the step is $42,590. Someone in a first analyst or technician-level scientific role can set the conversation beside $59,330. Someone doing independent scientific work at a typical full scope can set it beside $101,920 and ask how the offer compares with that national midpoint. The $42,590 is the published step between those anchors. It does not auto-convert into a promotion schedule.

Texas appears as two different statistics. The high end of the published range in Texas is $287,180. Texas's median is $145,220. The high end is the top of the range. The median is the state's midpoint. They share a state and they do not share a meaning. Quoting $287,180 as the pay a typical Texas oceanographer should expect will embarrass you in front of anyone who can read the table. Quoting $145,220 as the high end will undersell a role that truly sits at the top of the range. Texas's median sits $43,300 above the national median. That $43,300 is the midpoint comparison. From the national median up to the Texas high end, the distance is $185,260. That second distance belongs only in a sentence about the top of the published range.

Other state medians are midpoints and should stay in midpoint sentences. Oklahoma's median is $119,990. Colorado's median is $114,410. California's median is $106,500. Washington's median is $105,020. Pennsylvania holds the lowest median in the set, $84,180. The gap between the highest state median and the lowest state median is $61,040. The gap is useful when you are choosing among regions and useless as a demand that one lab leap its pay by the whole spread. If the job is in Texas, you may cite $145,220 as the state median and, in another sentence, $287,180 as the high end if the role's scope earns that label. Oklahoma, Colorado, California, and Washington each contribute a median only, not a high end.

Bring one labeled figure to the offer. Early scientific support points at $59,330. Established practice points at the national median of $101,920, adjusted in the conversation by a state median when the state is one of these five. Keep Texas's high end in its own sentence. Then talk about the problem you will actually work, the writing you will be expected to finish, and whether the funding covers the length of the project. Institutions pay for that finished work. They do not pay extra for a tour of every state median you can recite.

The top of Oceanographer pay — and how to get there with AI

$287,180what Oceanographer pay reaches in Texas

Highest state-level top-of-range annual wage for Geoscientists, Except Hydrologists and Geographers, 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 — Data Scientists — reaches $224,920 in California.

$59,330entry$101,920middle$287,180top end

In this occupation the pay difference is rarely about the science; it is about sitting close to funded survey programmes and being able to take a cruise or a contract at short notice.

Most oceanographers spend a career wherever their first institution happened to sit. Planning and running field studies, collecting samples, measuring gravity and magnetic fields with gravimeters and magnetometers, then turning all of it into charts and cross-sections is funded very unevenly from one region to the next. What has changed is how portable the second half of that chain has become. A model that reads your processing scripts and drafts the routine sections of a technical report around them lets you carry a working method between employers instead of relearning somebody else's.

Your playbook, by where you are now

Just startingGet sea time and keep your own record

  1. Volunteer for every cruise, drilling programme and shore-based sampling campaign your group runs, and take the instrument watches nobody else wants.
  2. Write down each calibration, each failed cast and each station where the gravimeter or magnetometer misbehaved, because that log is the part of your experience an employer will ask about and cannot verify.
  3. Process your own data in a scripted pipeline instead of by hand, and keep it under Git so it travels with you.
  4. Learn ESRI ArcGIS software well enough to produce a chart or cross-section yourself the week the data lands.
  5. Ask what each survey cost and who paid for it, so you learn which questions attract funding.

What proves it: A campaign record plus a processing pipeline you can run on a new employer's data within a day.

Realistic span: the first two or three years after your degree

A few years inMake the second half of the job portable

  1. Package your processing and quality checks so they run on an unfamiliar dataset without a rewrite, and document every assumption.
  2. Have Claude or ChatGPT draft the routine sections of a survey report from your processing notes, then check each figure against the data yourself.
  3. Put your group's past technical reports into NotebookLM so you can find precedent for how a result was worded, verifying each answer against the filed report.
  4. Take one contract or secondment outside your home institution, however short, so you learn what a rate looks like and what a client expects.
  5. Present a method rather than a single result, because methods travel between employers and results do not.

What proves it: A completed contract or secondment outside your home institution, with a written method others reused.

Realistic span: years three to seven

ExperiencedMove to the work, or let the work come to you

  1. Track where subsurface and offshore survey programmes are actually commissioned; Texas concentrates a great deal of that activity and salaries follow the concentration.
  2. Choose deliberately between staff and contract work, since contract pays for scarcity and travel while staff pays for continuity.
  3. Take charge of planning survey, drilling and testing programmes across projects, because the person who designs the campaign is the person the budget is written around.
  4. Build the reproducible-pipeline, version-control and statistics habits that a data scientist is paid for, since that is the nearest better-paid neighbour to this work.
  5. Keep one strand of hazard work running, such as estimating risk from seafloor instability or earthquakes, because risk questions get funded when exploration does not.

What proves it: A survey programme you designed and costed, delivered in a location you chose.

Realistic span: eight years and beyond

The next 90 days

Take the last survey you worked on and rebuild its processing from raw data as a scripted pipeline a stranger could run. Document the calibration decisions, the corrections applied and the reason for each one, then produce the chart or cross-section from that pipeline end to end. Ninety days later you own something you can demonstrate in an interview and run on somebody else's data in an afternoon. While you build it, telephone two people doing similar survey work in another region and ask what the day rate is, how long the mobilisations last, and whether they are hired as staff or on contract. Portability and information about where the work sits are the two things that decide whether relocating or going contract is worth it for you.

Wage figures: BLS OEWS, May 2025. The playbook is PayCrunch editorial guidance, not a guarantee of pay or placement.

Careers related to Oceanographer

Similar pay, same field

Where this can lead

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 pairing Python with an AI coding assistant. Ocean data lives in large NetCDF and array datasets, and the fastest skill upgrade is analyzing them fluently. Open a Jupyter notebook, use GitHub Copilot or Claude to write and explain xarray/pandas code, and you will move through datasets that used to take colleagues weeks - the exact skill that commands industry pay.

For free leverage, use ChatGPT or Claude to draft and debug analysis code, Elicit or Consensus for literature, and open data from Copernicus Marine, Argo, and NOAA ERDDAP to practice on. Keep proprietary or embargoed survey data inside approved systems, and always validate AI code against known results before you trust it.

The one rule, forever: Scientific integrity governs everything. Validate AI-written code and model output against physical oceanography, instrument calibration, and ground-truth measurements before trusting a result, and never let a plausible-looking figure substitute for that check. AI fabricates citations and can smooth over data problems - verify every reference and preserve full data provenance. For consulting work, keep embargoed or proprietary survey data out of consumer AI tools.
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
Accelerate data-analysis pipelines with AI-written code
Why this pays: Speed and fluency with big ocean datasets is the single most transferable, best-paid skill in the field. An oceanographer who builds analysis pipelines fast is the one who lands the data-heavy industry role and out-produces peers on grants and papers.
Python (xarray, pandas)GitHub CopilotClaude
1
In a Jupyter notebook with GitHub Copilot, write and comment your analysis, letting it autocomplete the boilerplate for loading and reshaping NetCDF/array data.
2
Get working, documented code for a specific analysis task.
Copy-paste this prompt
Write documented Python using xarray to load a [sea surface temperature] NetCDF dataset, compute the [monthly climatology and anomaly] over [1990-2020] for the region [lat 30-45N, lon 60-40W], and plot the anomaly time series with a trend line. Explain each step and note assumptions I should check.
Validate on a region with a known answer before trusting it; AI can silently mishandle grids, missing values, or coordinate conventions.
3
Paste errors and unexpected results back into Claude to debug, and ask it to sanity-check units and coordinate systems against physical expectations.
What you'll haveAnalysis that used to take weeks done in days, with reproducible, documented code.
2
Apply machine learning to ocean data
Why this pays: Predictive and classification models - forecasting blooms, gap-filling floats, detecting fronts - are a premium skill industry pays for directly. Being the oceanographer who can build and validate them is a clear route to the $287,180 tier.
scikit-learnPyTorchGitHub Copilot
1
Start with scikit-learn for tractable problems - regression and classification on in-situ and satellite data - using Copilot to scaffold the pipeline.
2
Design a defensible ML approach before coding.
Copy-paste this prompt
I want to predict [chlorophyll-a concentration] from [SST, salinity, and light] using [Argo and satellite] data. Recommend a modeling approach for a scientist: which algorithms to try, how to split data to avoid spatial and temporal autocorrelation leakage, which metrics to report, and the physical sanity checks I should run on predictions.
Guard against leakage from autocorrelated ocean data, and always check predictions against physical limits and held-out ground truth.
3
Move to PyTorch for spatial or sequence problems, keeping every model validated against independent measurements, not just training-set metrics.
What you'll haveA validated predictive model - the deliverable that opens industry and grant funding.
3
Mine the satellite and geospatial record
Why this pays: Satellite analysis at scale - decades of global ocean-color, altimetry, and SST - is exactly what offshore-wind siting, climate-risk, and monitoring contracts need. Fluency in cloud geospatial tools is billable expertise.
Google Earth EngineQGISChatGPT
1
Use Google Earth Engine to process planetary-scale satellite archives in the cloud without downloading terabytes, and QGIS to finish maps and layers.
2
Get Earth Engine code for a specific coastal analysis.
Copy-paste this prompt
Write Google Earth Engine JavaScript to compute a [10-year] time series of [sea surface temperature] and [chlorophyll] for the coastal box [coordinates], mask clouds and land, aggregate to monthly means, and export a CSV. Comment each step and note the dataset and units used.
Confirm the dataset's resolution, units, and quality flags are appropriate for coastal work before relying on the output.
3
Overlay results with bathymetry and habitat layers in QGIS to produce the maps that consulting and siting reports are built on.
What you'll haveCloud-scale satellite analysis you can turn into billable coastal and offshore reports.
4
Automate survey imagery and acoustic classification
Why this pays: Environmental and offshore surveys generate mountains of seafloor imagery and hydroacoustic data that must be classified - traditionally by hand. Automating it with computer vision is a high-value, contract-winning capability in marine consulting.
FathomNetVIAMERoboflow
1
Use VIAME (NOAA's open marine vision toolkit) and FathomNet's annotated marine imagery to build and train detectors for benthic organisms and features.
2
Stand up a custom classifier fast with Roboflow for annotation and model training on your own survey images.
3
Plan an annotation and validation protocol before you scale.
Copy-paste this prompt
I have [50,000] seafloor images from an [ROV survey] and need to detect and count [target species]. Design a workflow: how many images to hand-annotate for training, how to split for validation, how to measure detector precision and recall, and how to QA the automated counts before reporting them.
A human expert must verify a sampled subset of AI classifications before results go in a regulatory or scientific report.
What you'll haveSurvey data classified in a fraction of the time - a service consulting clients pay for.
5
Synthesize literature and win proposals faster
Why this pays: Funding is the currency of a science career, and grant success separates lead scientists from staff. AI-accelerated literature review and proposal drafting means more, stronger submissions - the engine behind promotions and top-of-range academic pay.
ElicitConsensusNotebookLM
1
Use Elicit and Consensus to map the literature on a question fast, extracting methods, sample sizes, and findings into a comparison table.
2
Load key papers and your own past proposals into NotebookLM and draft sections grounded in those documents.
Copy-paste this prompt
Using the attached papers and my prior proposal, draft the 'Background and Significance' section for a grant on [the role of mesoscale eddies in carbon export]. Make it rigorous and specific, cite the attached sources, and flag any claim that needs a citation I have not provided.
Verify every citation exists and supports the claim - AI invents references. Never upload unpublished collaborators' data without permission.
3
Have AI critique your specific aims for weaknesses a reviewer would flag, then revise.
What you'll haveMore competitive proposals submitted per year - the driver of funding and rank.
6
Publish and communicate results faster
Why this pays: Publications and clear communication build the reputation that earns senior roles and consulting work. AI-assisted writing and figure-making shorten the slowest part of the pipeline, so you ship more high-quality output.
OverleafChatGPTPython (Matplotlib)
1
Draft and revise manuscripts in Overleaf, using AI to tighten prose, restructure a muddled results section, and adapt text to a journal's format.
2
Generate publication-quality figure code and captions.
Copy-paste this prompt
Write Python (Matplotlib/Cartopy) to make a publication-quality map of [surface current vectors over SST] for the region [coordinates], with a proper projection, colorbar, scale, and land mask. Then draft a concise figure caption describing what it shows.
Check that the figure represents the data honestly - correct projection, scale, and uncertainty - before submission.
3
Use AI to draft plain-language summaries and slide outlines for stakeholders, funders, and the public.
What you'll haveMore papers and clearer communication - the reputation behind senior pay.
Your 12-month sequence to the top of the range

How the plays above stack into a path from median pay toward the $287,180 tier.

Month 1
Get fluent analyzing ocean data in Python with Copilot or Claude on open datasets (Copernicus Marine, Argo, NOAA ERDDAP), validating every result.
Months 2-3
Add cloud satellite analysis in Google Earth Engine and start a machine-learning project on a well-understood prediction or classification problem.
Months 3-6
Build a computer-vision workflow for survey imagery (VIAME, Roboflow) and accelerate your literature reviews with Elicit and Consensus.
Months 6-12
Turn the skills into output - submit stronger proposals, ship papers with AI-assisted writing and figures, and package a consulting-ready capability.
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.

McKinney Python for Data Analysis, 3rd

Same live O’Reilly 3rd already on data-scientist / python-developer / market-research-analyst / biochemist / botanist. This page names Python (xarray, pandas) as a play tool and the upgrade is write and explain xarray/pandas code. Not CompTIA Data+ and not leftover Ross Exam P (that is actuary).

Next steps for an Oceanographer

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.

Oceanographer work is specific enough that a stamped 'check out these courses' block would be noise. BLS files this work as Geoscientists, Except Hydrologists and Geographers (SOC 19-2042). O*NET Job Zone 5 is typical: graduate or professional school, so the honest next credential is a graduate-level or professional certificate — not a random catalog dump.

The occupation's listed knowledge areas include Geography and Chemistry; the links search those subjects, not a generic 'career courses' list.

Oceanographers in this dataset list Autodesk AutoCAD among the tools in use, so a program that names that stack is a better fit than a survey course.

Geography programs on Coursera for Oceanographer work

Coursera search for geography — a graduate-level or professional certificate that lines up with science, not a generic professional-development aisle.

Geography courses on edX

edX search for geography, aimed at science (SOC 19-2042). Same field as the Coursera link, different university catalog.

Screened remote and flexible Oceanographer listings on FlexJobs

FlexJobs screens remote, hybrid, freelance, and flexible listings so you are not wading through unverified ads. This is a job-board search for Oceanographer work, not a claim that they list a counted SOC 19-2042 inventory.

Build an Oceanographer resume on Resume Now

Write an Oceanographer resume, or one aimed at Data Scientists, instead of a blank template. Resume Now is a resume builder; we are not claiming a counted template set for this SOC.

Build an Oceanographer resume on Zety

An Oceanographer resume that names the actual tasks on this page, or the step-up title Data Scientists, beats a blank template when you apply.

What Oceanographers earn by state

These are the Bureau of Labor Statistics’ own figures for Geoscientists, Except Hydrologists and Geographers, state by state — not a cost-of-living adjustment applied to the national number. Only states employing at least 500 people in the occupation are shown, because a state median drawn from a handful of workers is noise rather than a signal.

Texas
$145,220
highest of them · +42% vs the national median
Pennsylvania
$84,180
lowest of the 11 states that qualify · -17% vs the national median
The same job pays $61,040 more a year at the median in Texas than in Pennsylvania — 73% higher. That gap is what the Bureau measured, before any question of what it costs to live in either place. Texas also carries the top of this job’s range, $287,180 — the figure quoted at the head of this page.
Texas$145,220Oklahoma$119,990Colorado$114,410California$106,500Washington$105,020Nevada$99,890North Carolina$95,260Florida$95,140

Source: U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025, SOC 19-2042. 11 states clear the 500-employee reporting floor for this occupation; those below it are left out rather than shown with a wide error band.

Free data. Use any of it.

PayCrunch publishes verified, BLS-sourced salary + AI-playbook data on 1,000+ professions — free, no signup.

Frequently asked
Will AI replace oceanographers?
No. AI cannot design a field campaign, deploy instruments, judge whether a result is physically plausible, or take responsibility for a scientific conclusion. It is a powerful accelerator for the computational and writing work. The oceanographers gaining ground are those who use it to analyze more data and publish more; the risk is being out-competed by them, not replaced by a model.
Do I need to be a programmer to use AI in oceanography?
You need to be comfortable reading and validating code, but AI has lowered the barrier to writing it. With Copilot or Claude you can produce working Python and Earth Engine analyses while you build fluency - as long as you check every output against known results and physical reasoning rather than trusting it blindly.
Can I trust AI-generated analysis code and citations?
Only after you validate it. AI code can mishandle grids, missing values, or units in ways that produce a plausible but wrong figure, and AI routinely invents references that do not exist. Test code on data with a known answer, and confirm every citation against the actual paper before it enters your work.
How does AI actually raise an oceanographer's pay?
The top salaries are in industry - offshore wind, climate-risk, insurance, and environmental consulting - and in grant-winning lead-scientist roles. All of them reward exactly the data-analysis, machine-learning, satellite, and computer-vision skills AI helps you build fast. Those capabilities are the bridge from an academic salary to the top of the range.
Which AI tool should I start with?
GitHub Copilot or Claude for Python data analysis, because fluency with large ocean datasets underpins every higher-paying path. Once that is second nature, add Google Earth Engine for satellite work and a machine-learning project to your portfolio.
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.

Sources