The risk analyst who converts speed into a bigger remit
$144,030estimated top of the range · middle $78,000 / yr
AI augments this role
Risk Analysts in the United States earn a median of $78,000 a year. Pay starts near $50,000. The top of the range is estimated at $144,030. The Bureau of Labor Statistics does not publish a separate wage series for this exact title, so this figure is derived from the closest occupation it does track and is labelled an estimate.
Source: PayCrunch estimate. Last checked 9 September 2026.
Entry level
$50,000
Top-end estimate
$144,030
Education
Bachelor's degree in Finance or Statistics
Wages — PayCrunch estimate. The Bureau of Labor Statistics does not publish a separate wage series for Risk Analyst; figures are derived from the closest occupation it does track and are labelled as estimates. AI-impact rating is PayCrunch's editorial assessment. Updated September 2026.
🆕 New & Trending AI Tools for Risk AnalystReviewed September 2026
We track new AI-tool launches every week and refresh this list — here’s what’s gaining traction for Risk Analyst work right now.
NumericNEWPaid / see site
AI-driven month-end close, reconciliation, and reporting.
How a Risk Analyst uses it: automate reconciliations and close the books faster
HebbiaNEWEnterprise / see site
AI that reads and analyzes large financial documents and filings.
How a Risk Analyst uses it: pull answers out of contracts, filings, and reports in minutes
NotebookLMNEWFree / $7.99 mo
Google tool that answers questions grounded only in the documents you give it — with citations.
How a Risk Analyst uses it: load your own manuals, policies, or PDFs and ask questions that stay accurate to the source
MindBridgeEnterprise / see site
AI that scans transactions for anomalies, errors, and fraud risk.
How a Risk Analyst uses it: flag risky or unusual entries across the whole ledger, not just a sample
Vic.aiEnterprise / see site
Autonomous accounts-payable and invoice processing.
How a Risk Analyst uses it: let AI code and process invoices with minimal manual entry
RampFree core / paid
Finance platform with AI that automates expenses and spend controls.
How a Risk Analyst uses it: auto-categorize spend and catch policy issues in real time
Power BI Copilot$10+ mo
Microsoft analytics with AI that builds dashboards and explains trends.
How a Risk Analyst uses it: ask questions of financial data and get charts and forecasts back
ChatGPTFree / $20 mo
The most-used AI assistant — writing, analysis, research, and images from a plain-language chat.
How a Risk Analyst 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 Risk Analyst uses it: analyze big reports or spreadsheets and turn messy notes into clean, finished writing
The work behind the title
A risk analyst spends the week looking at what could go wrong and writing it so someone with authority can decide. The seat might sit in a bank, an insurer, a hospital system, a manufacturer, or a company that simply got large enough to want a second pair of eyes. The subject changes. Credit. Operations. Vendors. A product launch. A control that exists on paper and not on the floor. The craft does not. You gather the facts, you name the exposure in plain language, and you stop before you pretend to be the person who accepts the risk. That person is usually a manager or a committee. You are the one who makes their decision possible.
A normal day is reading and asking, then writing. Policies, incident logs, a portfolio summary, a process map someone drew and nobody updated. You notice where the story is thin. You talk to the people who actually do the work, because a control described by a distant office and a control described by the clerk are often two controls. Then you draft a finding: what you looked at, what you saw, how serious it seems, and what you recommend someone consider. Recommendations at this level are options and consequences. They are not a manual for taking positions, and they are not a set of steps for getting around a control. If your draft starts to teach either of those, delete it and return to the finding.
The audience is the point. A trader, a plant manager, a chief financial officer, and a board committee do not read the same way. You learn the vocabulary of the business well enough to be accurate, and you refuse the vocabulary when it is only there to fog the result. Short sentences. A ranked list. A sentence that says what is unknown. Analysts who hide a scary result in an appendix get found out, and the finding costs more than the honesty would have. Analysts who dramatize a small miss get tuned out the next time, when the miss is real.
A report a committee can use
Most of the job lands in a report or a memo on a regular rhythm. Monthly for a portfolio. Quarterly for an enterprise view. Immediately when something broke. The report should tell a busy person three things: what changed, what matters, and what decision is sitting there unanswered. Charts help when they carry one claim. Charts hurt when they are a mood. Write the claim in a line above the picture. If you cannot, you do not understand the picture yet, and the committee will notice before you do.
You will sit in the meeting even when you do not run it. Your task is to defend the facts and to correct a misreading while it is still small. Speak once, clearly, and then stop. If a senior person wants a different emphasis, you can change the emphasis only if the facts still hold. You do not sand the numbers so the meeting feels better. That habit is how risk functions become decoration. Keep a record of what the committee actually decided. The next report should say whether the decision happened in the world, or only as a remark in the room that nobody wrote down. Call it a decision, or call it unfinished. Do not call it both.
Some analysts support a desk that watches limits. You track whether activity stays inside a boundary someone else set. You flag the breach. You do not invent a new boundary in the hallway, and you do not explain how to structure a trade so the boundary fails to see it. The career described here is the watch and the write-up. Firms that want a trading playbook are asking for a different job. If an interview drifts that direction, bring it back to a finding you wrote and a decision someone made because of it.
Preparation hiring teams recognize
A bachelor's degree in finance, economics, statistics, mathematics, or business is the usual door. The degree proves you finished a curriculum that included quantitative work and, often, writing about it. It does not prove you can brief a skeptical manager. That proof is a work sample: a memo with the confidential figures removed, a class project with a clear recommendation, or a process review from a prior job. Lead with the conclusion. Then show the evidence. People who lead with software names and never reach a conclusion blend into the pile.
Some analysts later pursue a professional certificate from a risk society. The society grants it. It proves you completed that society's program. It does not, by itself, make you the person a firm will trust with its first look at a new exposure. Take it when you want a shared language and you already have a sample. Skip it as a costume. Internal training at the employer matters more in the first year: their definitions, their appetite statement, their template for a finding. Learn those before you argue with them. You can improve a template after you have used it honestly.
Keep this title separate in your own head from the financial risk specialist occupation. A risk analyst, in the sense of this career, may work on operational problems, credit files, vendors, or enterprise reviews. Specialists who live on a market desk are a neighboring occupation with their own published wages, and those wages are not the ones below. If you want that specialist path, say so and prepare for it directly. If you want this analyst seat, prepare the memo. Mixing the two titles in an interview makes you sound like you read a list of jobs and not a single one of them.
Getting hired into the seat
Hiring managers look for calm and for a trail. Internships in audit, credit, compliance-adjacent teams, or a finance rotation all count. So does a first job where you already wrote findings, even under a duller title. In the interview, expect a messy scenario described in words. They want to hear what you would ask, what you would refuse to assume, and how you would write the result. Think aloud. State the assumption. State what would change your mind. People who leap to a dramatic conclusion lose to people who can outline a careful one.
Bring references who saw the writing, not only a professor who liked your exam. A manager who used your memo is ideal. Be ready to explain a time you were wrong and how the next draft changed. Risk work without that story sounds fictional. Also be ready to say which industry you actually understand. A hospital's operational risks and a lender's credit risks share a discipline and not a calendar. Claiming both fluently, with no scar tissue in either, is a tell. Pick the room you can describe, and let the method be what transfers. The first year is mostly vocabulary and trust. You learn which number the business treats as official, which log is a fiction, and which manager wants the ugly finding early. Write those lessons down where you can reuse them. A finding that surprises your own boss in a committee is a failure of timing, even when the finding is correct. Share the draft upward before it travels outward. That habit is dull. It is also how analysts keep their seat long enough to deserve a wider one.
Growing past the first title
The near step is senior analyst, then a lead who reviews other people's findings, then a manager who owns the committee pack. Some people move from an analyst seat into audit, into a business unit as the risk partner, or into a specialist track if that is truly the work they want. Scope is the promotion: a wider set of exposures, a signature on the report, a voice the committee expects. A new title over the same narrow file is a compliment. Ask what decision you will newly influence, and what you will still be forbidden to decide alone. The second answer is healthy. Analysts who want to accept risk on their own have misunderstood the chair.
Keep a private record of findings that changed something. A control that was fixed. A limit that was clarified. A product launch that waited until a gap was closed. Strip the confidential details and keep the shape of the story. That record is how you ask for the next seat, and it is how you explain your pay. Curiosity about the business helps as much as another course. Sit with the operators long enough that your findings sound like their work. Analysts who only talk to other analysts write beautiful memos nobody can use. A useful stretch, once the first title feels steady, is to own the follow-up. Not a new theory. A check, weeks later, on whether the fix arrived and whether it created a fresh problem beside the old one. People who close that loop become the analysts a committee trusts. People who only open findings become a source of noise. Trust is the promotion, and it is built one finished loop at a time.
Why these wages are estimates
A separate title, a separate estimate
The Bureau of Labor Statistics does not publish a separate wage series for this exact title. PayCrunch estimates the figures below. They stand apart from the Financial Risk Specialists occupation, and they are not tied to any state.
The estimated entry level is $50,000. The estimated median is $78,000. The step between them is $28,000, which is the distance from a new analyst under close review to someone who can send a finding without it being rewritten from scratch. The estimated top is $144,030. From the median up to that top is $66,030, a stretch that belongs to broader scope, a harder book of exposures, or a lead role, not to a first memo. Use the three figures as a national spine for the risk analyst title. Do not redraw them as a hometown rate. The estimate cannot support that redraw, and it should not be decorated with wages from a neighboring occupation.
Say the labels every time. $50,000 means entry. $78,000 means the middle of this PayCrunch estimate. $144,030 means the estimated top. A firm that quotes the top for a seat that still requires every sentence to be approved is borrowing a landmark it has not earned. A firm that quotes entry pay for someone who already owns the committee pack is under the middle of the estimate by a gap you can point at. Bring the scope of the findings. Then bring $78,000 as the middle. Let the top wait until the scope is actually wide.
A salary talk inside the estimate
Ask for the conversation after a report has landed well, not in the week a finding embarrassed someone. Put the offer next to $50,000, $78,000, and $144,030 and ask which scope the firm thinks it is buying. If you are new, entry pay plus a clear reviewer is a fair shape, and you should be able to describe what "reviewed" means: every memo, or only the ones that go upward. If you already write the upward memo, the national middle of this estimate is the honest spine. The $28,000 between entry and the median is the learning distance. Walk it with examples, not with adjectives.
Leave the estimated top of $144,030 for a role that already looks like leadership over the work: several analysts, a pack the committee expects from you, a book of exposures wider than one process. Bonus plans and long-term pay exist and sit outside this estimate, so ask what they are without inventing a dollar value. Then repeat the reason the estimate exists at all. The Bureau of Labor Statistics does not publish a separate wage series for this exact title, so PayCrunch estimated these three figures, apart from the Financial Risk Specialists series. Use them as a guide. Use the findings you have actually changed as the evidence. A memo that moved a decision is still the strongest sentence in the room.
The top of Risk Analyst pay — and how to get there with AI
$144,030top-end estimate for Risk Analyst
PayCrunch estimate - derived from the closest occupation BLS tracks (Operations Research Analysts, 15-2031). This figure is PayCrunch’s estimate, not a Bureau of Labor Statistics published wage for this exact title.
And the role it leads to — Software Developers — reaches $272,670 in California.
$50,000entry$78,000middle$144,030top end
The risk analysts sitting at the top of this range were not the fastest modellers; they used the time modelling stopped taking to move upstream, into deciding which problems get modelled at all.
Formulating simulation models, specifying computational methods, validating and reformulating them when they fail, then writing the management report defining the problem and recommending a course of action: the middle of that chain has become much cheaper. Code assistants such as GitHub Copilot and Cursor handle scaffolding, Bash glue and the fifth rewrite of a Redshift query. The framing and the validation have not become cheaper at all. Analysts who bank the saved hours as extra throughput stay where they are. Analysts who spend them observing the operation in person, gathering component problems from source, and educating staff in using the models are the ones asked what should be modelled next.
Your playbook, by where you are now
Just startingGet fast, and measure how fast
Time your build cycle from problem statement to first validated model, and keep timing it as you improve.
Use GitHub Copilot or Cursor for query and pipeline scaffolding against Amazon Redshift or Apache Hive, and review every line before it runs.
Put your work under version control in GitHub from the first day, including the failed formulations.
Write the validation and testing step into your own process rather than treating it as something reviewers do.
Read the research literature in your problem domain weekly, since the constraint on a model is usually knowledge, not code.
What proves it: A version-controlled model with its validation history and a recorded build time.
Realistic span: years one to three
A few years inSpend the hours upstream
Go and observe the system you model in operation, and gather the component problems from the people living with them.
Turn one management report into a decision memo that lays out alternative courses of action and what each would cost.
Teach two colleagues to run and interpret your model, then step back and let them.
Keep a written record of every recommendation you made, what was decided, and what happened afterwards.
Take the forecasting or spatial problems others avoid, using Business Forecast Systems Forecast Pro or ESRI ArcGIS software as the situation demands.
What proves it: A decision that was made differently because of a model you framed rather than one you were handed.
Realistic span: years four to seven
ExperiencedAsk for the remit, with evidence
Present the throughput record and the decision history together, because either one alone is easy to dismiss.
Take responsibility for a portfolio of questions rather than a queue of models, including which ones to decline.
Set the validation standard your team works to and audit against it.
Note where the demand concentrates; the District of Columbia pays this occupation the most, on the strength of federal and policy modelling work.
Move toward engineering ownership of the models in production if the software route appeals, since that is the common step up from analysis.
What proves it: A named remit over a problem area, with a team using your validation standard.
Realistic span: eight years and beyond
The next 90 days
Pick your next model and run two clocks. The first is build time: from the moment the problem is stated to the moment a validated version exists. The second is what you did with the difference between that and your usual build. Deliberately spend the recovered days on the front of the problem instead of starting the next ticket, watching the actual operation, interviewing the people who raised it, and writing down the alternatives you rejected and why. Then present the model with that framing attached. Do this three times and you will have both halves of the argument: proof you are faster, and proof the extra time produced better questions.
Wage figures: PayCrunch estimate. 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 making the math and code faster. Open GitHub Copilot (or Claude) alongside Python and use it to write and debug analysis - a VaR calculation, a credit-scoring model, a Monte Carlo simulation - explaining each line so you can validate it. If you're Excel-based, use Microsoft Copilot to build and audit models and to write the formula you're stuck on.
To learn, use ChatGPT or Claude (with no proprietary data) to explain a risk concept, derive a metric, or plan an FRM study schedule. Keep all real positions, exposures, and portfolio data inside your firm's approved, secured systems.
The one rule, forever: Models are decision-support and must be governed. Validate, backtest, and document every model to model-risk standards (in the U.S., the Fed's SR 11-7); an unvalidated AI-generated model can produce real losses. Independently verify all AI-written code and formulas - AI hallucinates plausible-but-wrong math. Never paste proprietary positions, MNPI, or confidential portfolio data into a consumer AI tool; use approved, access-controlled environments.
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
Build and backtest models faster with AI-assisted code
Why this pays: The analyst who can build, test, and iterate models quickly does more and better risk work than one hand-coding everything. That productivity - more models validated, more insight delivered - is what earns the senior and quant titles in the $118,000 band.
Python (pandas, scikit-learn)GitHub CopilotClaude
1
Use GitHub Copilot in your notebook to scaffold a model - data cleaning, feature engineering, a credit-default or VaR calculation - then read and validate every line before trusting it.
2
Turn a method into working, checkable code.
Copy-paste this prompt
Write commented Python to calculate 1-day 99% Value at Risk for a portfolio using both the historical-simulation and variance-covariance methods, then compare them. Explain the assumptions and limitations of each, and add a backtesting function to count exceptions. Use a sample dataframe - I'll plug in real data in my secure environment.
Verify the math and logic yourself; run it only on real data inside your firm's approved environment, never a consumer tool.
3
Have AI generate unit tests and edge-case checks so your model behaves correctly on stressed inputs, not just normal ones.
What you'll haveMore models built, tested, and validated per quarter - the analytical output that moves you from junior to senior quant pay.
2
Run richer stress tests and scenario analysis
Why this pays: Leadership pays for foresight - knowing what breaks the portfolio before it happens. An analyst who can generate and quantify a wide range of scenarios fast delivers exactly that, the high-visibility work that gets you noticed and promoted.
PythonChatGPT@RISK
1
Use ChatGPT or Claude to brainstorm a comprehensive scenario set - macro shocks, rate moves, sector contagion - then implement the quantification in Python or @RISK Monte Carlo.
2
Design a defensible stress-testing framework.
Copy-paste this prompt
Act as a risk modeler. Design a stress-testing scenario set for a [commercial loan portfolio]: list plausible severe-but-realistic macro scenarios, the risk factors each stresses, the variables to shock and by how much, and how to present results to a risk committee. Explain the rationale. General framework - no portfolio data.
Use AI to widen your imagination and structure; calibrate severities to your real book and validate the model before relying on it.
3
Have AI help translate results into a clear committee narrative - what breaks, at what threshold, and the recommended mitigation.
What you'll haveBroader, sharper scenario analysis delivered fast - the foresight leadership rewards with advancement.
3
Automate risk reporting and dashboards
Why this pays: Analysts lose days to repetitive reporting. Automating it frees time for real analysis and makes you the source of clear, timely risk insight - the reliability and value that separates a report-runner from a trusted advisor.
Power BIMicrosoft CopilotPython
1
Build an automated risk dashboard in Power BI (limits, exposures, KRIs, breaches) and use Copilot to query it in plain English and to draft the commentary.
2
Turn numbers into an executive summary.
Copy-paste this prompt
Summarize this risk data into a concise executive risk report: the top 3 exposures, any limit breaches, the trend versus last period, and a clear recommendation for each. Keep it factual and decision-oriented. Data: [paste aggregate, non-confidential figures]. About 250 words.
Use aggregate, non-sensitive figures; you own the interpretation and recommendations, not the AI.
3
Script recurring reports in Python so they refresh automatically, and spend the reclaimed time on analysis leadership actually reads.
What you'll haveReporting that runs itself and reads clearly - more time for high-value analysis and a reputation as a trusted advisor.
4
Accelerate research and credit and market intelligence
Why this pays: Faster, deeper research means better-informed risk views. An analyst who can synthesize market, credit, and news signals quickly brings insight others miss - the edge that earns a senior, decision-shaping seat.
Bloomberg TerminalPerplexityChatGPT
1
Use the AI features in the Bloomberg Terminal for market data and news synthesis, and Perplexity for sourced research on a sector or counterparty (public information only).
2
Structure a fast credit review.
Copy-paste this prompt
Act as a credit analyst. From this public company's financials, outline a credit assessment: the key leverage and coverage ratios to compute, trends and red flags to check, the main risks, and questions I should investigate further. Financials: [paste public figures]. General framework - I'll verify.
Use only public data; treat AI output as a starting checklist and verify every figure and conclusion yourself.
3
Have AI summarize long rating-agency or research reports into the few points that change your risk view.
What you'll haveDeeper research delivered faster - the informed, differentiated risk views that earn a senior seat.
5
Specialize, get FRM-certified, and lead model validation
Why this pays: The top of the band belongs to specialists - model risk, credit, market, or quant. AI compresses the path: it tutors you toward the FRM and helps you produce rigorous validation work, the credential-plus-skill mix behind pay at the top of the range.
NotebookLMChatGPTPython
1
Pick a specialty (model risk is especially in demand) and use NotebookLM to study the frameworks (SR 11-7, Basel) and ChatGPT to quiz you toward the FRM.
2
Produce a rigorous model-validation review.
Copy-paste this prompt
Outline a model-validation report for a [credit scoring] model consistent with SR 11-7: the sections to cover, the conceptual-soundness checks, the performance and backtesting tests to run, and the limitations to document. Explain what a strong validation demonstrates. General template only.
Validation must be independent and evidence-based; use AI for structure, do the testing on governed data, and document thoroughly.
3
Volunteer to lead a model-validation or model-risk project - the high-rigor, high-visibility work that earns the senior title.
What you'll haveA validated specialty plus the FRM - the credential-and-skill combination that reaches the top of the risk-analyst pay band.
Your 12-month sequence to the top of the range
How the plays above stack into a path from median pay toward the $118,000 tier.
Month 1
Pair Copilot or Claude with Python (or Excel) to build and validate one model faster; verify every line. Reclaim your coding time.
Months 2-3
Automate a recurring risk report in Power BI and expand your stress-testing scenario set with AI.
Months 3-6
Speed research with Bloomberg and Perplexity; start the FRM with AI as your study coach and pick a specialty.
Months 6-12
Lead a model-validation project and go deep in model, credit, or market risk - the specialty that reaches $118,000.
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 / quantitative-analyst. This page names Python (pandas, scikit-learn) on Build and backtest models faster. Not leftover 94 CFP and not CFA Level I as the lead (that is financial-analyst / credit-analyst).
Next steps for a Risk Analyst
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.
Risk Analyst work is specific enough that a stamped 'check out these courses' block would be noise. BLS files this work as Operations Research Analysts (SOC 15-2031). 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 Engineering and Technology and Production and Processing; the links search those subjects, not a generic 'career courses' list.
Risk Analysts in this dataset list Amazon Redshift among the tools in use, so a program that names that stack is a better fit than a survey course.
Coursera search for engineering and technology — a graduate-level or professional certificate that lines up with computing, 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 Risk Analyst work, not a claim that they list a counted SOC 15-2031 inventory.
Write a Risk Analyst resume, or one aimed at Software Developers, instead of a blank template. Resume Now is a resume builder; we are not claiming a counted template set for this SOC.
A Risk Analyst resume that names the actual tasks on this page, or the step-up title Software Developers, beats a blank template when you apply.
What Risk Analysts earn by state
This page does not show a state table, and the reason is worth stating: the Bureau of Labor Statistics does not publish a separate wage series for this job title, so there are no official state figures to show. 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 $50,000, the median is $78,000, and the top of the range is $144,030. Those national figures are a PayCrunch estimate, not a Bureau of Labor Statistics published wage for this exact title.
No, though it changes the work. AI writes code, generates scenarios, and drafts reports, but risk is about judgment under uncertainty - choosing assumptions, weighing tail risk, questioning a model's limits, and owning the number that goes to the committee. Regulators require human accountability and independent validation. The analysts who use AI to build and validate faster become more valuable; those who only run yesterday's spreadsheet are the exposed ones.
Can I trust AI-generated model code or numbers?
Only after you validate them. AI writes plausible code that can contain subtle, costly errors, and it will state wrong formulas confidently. Read and test every line, backtest the model, and reconcile outputs against a known benchmark. In risk, an unvalidated model is a liability, not an asset - the discipline of verification is the job.
Is it safe to use ChatGPT for risk work?
Not with proprietary positions, MNPI, or confidential portfolio data. Use approved, secured environments (your firm's tooling, Copilot with data protection) for anything real, and reserve consumer tools for public data, generic code, and learning. Handling material non-public information correctly is both a legal duty and a career-defining habit.
How does AI increase a risk analyst's pay?
It multiplies your analytical output and frees you for judgment. Faster model building, richer stress testing, automated reporting, and quicker research let you deliver more and better insight, and they clear time to specialize. Pair that with an FRM and a specialty like model risk and you're operating at the senior, $118,000-plus level.
Do I need to learn Python?
It's the highest-leverage skill you can add, and AI makes learning it far faster - Copilot and Claude write, explain, and debug code with you. You don't need to be a software engineer, but a risk analyst who can build and validate models in Python, with AI's help, is dramatically more valuable than one confined to Excel.
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.