The pharmacologist whose assays other laboratories trust
$219,100top of the range in Kansas · middle $103,410 / yr
AI is transforming this role
Pharmacologists in the United States earn a median of $103,410 a year. Pay starts near $64,800. Pay reaches $219,100 at the top of the range in Kansas, 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 (Medical Scientists, Except Epidemiologists, SOC 19-1042). Last checked 9 September 2026.
Entry level
$64,800
Top of the range · Kansas
$219,100
Education
Doctoral degree in Pharmacology
Wages — U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025 (Medical Scientists, Except Epidemiologists). 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 PharmacologistReviewed September 2026
We track new AI-tool launches every week and refresh this list — here’s what’s gaining traction for Pharmacologist work right now.
Julius AINEWFree / $20 mo
AI data analyst that runs statistics and charts from plain-language prompts.
How a Pharmacologist 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 Pharmacologist 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 Pharmacologist 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 Pharmacologist uses it: get evidence-backed answers with the studies behind them
SciSpaceFree / paid
AI that explains papers and helps with literature review.
How a Pharmacologist 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 Pharmacologist 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 Pharmacologist 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 Pharmacologist 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 Pharmacologist uses it: draft and reply inside Google Workspace and research without leaving the page
A research day, told plainly
A pharmacologist studies how drugs act, how the body handles them, and whether a candidate is worth taking further. The morning is rarely a single dramatic result. You open the study that is already running, read what yesterday's samples did, and decide whether the pattern is real enough to show a colleague. Some of the work is at a bench. Some of it is a model on a screen. Some of it is a meeting where a chemist, a clinician, and a statistician need the same sentence from you: what this finding means for the program, and what it does not yet mean.
The subject is drug research, and the boundary around the job matters. You are not writing a recipe for making a compound, and you are not handing a clinic a dosing schedule. You are explaining action, exposure, and risk at the level a development team can use. That can mean comparing a new molecule with an older one, asking whether an effect seen in a model is likely to matter in people, or flagging a safety signal early enough that the program can slow down. The useful pharmacologist is specific. Vague enthusiasm does not help a team that has to choose which study to fund next.
The people around the work change with the employer. In a drug company you sit inside a project team with deadlines, a target product profile, and a regulatory path someone else is drafting with your input. In a university you may teach, mentor students, and chase the next grant while a lab keeps a line of inquiry alive for years. In a government or contract lab you may support studies designed elsewhere and still be the person who says the data are too thin. Across those rooms the daily craft is the same: frame a biological claim, test it with the methods your group already trusts, and write it so a stranger can follow the logic.
Documentation is half the profession even when nobody calls it that. Lab notebooks, study reports, and slides for a governance meeting are how your judgment leaves your head. A beautiful result that cannot be reconstructed is a story, not evidence. Senior people notice who writes clearly, who labels figures so they stand alone, and who will say "we do not know yet" when the room wants a yes. That habit is as hireable as any technique you learned in school.
The graduate degree that opens the door
Most pharmacologist postings expect a graduate degree. A doctorate in pharmacology, pharmaceutical sciences, or a closely related biomedical field is the common path into independent research. A master's degree can open research-associate and specialist roles, especially in industry, and some people later continue to a doctorate. A few come from a PharmD and move into research rather than dispensing. The degree is not a licence to practice pharmacy. It is evidence that you can carry a scientific project from a fuzzy aim to a defended result.
Preparation is the degree itself plus the project inside it. Admissions committees and later hiring managers look for coursework in physiology, drug action, and experimental design, and for a thesis or a set of studies you can explain without hiding behind jargon. You prepare by joining a lab early, learning how that lab keeps records, and finishing work you can talk about in plain language. Publications help. A clear account of a study that failed, and what you changed, helps too. Nobody needs you to recite a method as if it were a script. They need to hear that you understand why the study was built the way it was.
There is no single national board that grants the title pharmacologist the way a pharmacy board grants a pharmacist licence. Your authority comes from the degree, the reputation of the training lab, and the employer's confidence that you can be trusted with a program. If your work later touches human studies, the institution's review process and the clinicians on the protocol are the guardrails. You contribute the pharmacology. You do not freelance a human experiment because you hold a PhD. Knowing that limit is part of being ready for the job.
Who hires, and what they listen for
Drug makers, biotech firms, academic medical centers, government laboratories, and contract research groups all hire this skill, and they hire it under more than one job title. The posting might say pharmacologist, scientist, or principal scientist. Read the duties. If the work is drug action, disposition, safety pharmacology, or translational judgment, you are in the right family even when the headline uses a broader word. If the work is purely chemical manufacture, or purely clinical care, it is a different seat.
In a hiring conversation I want three things. First, a project you owned, told from the aim through the result, including the part that surprised you. Second, evidence you can work with people who do not share your training: a chemist who thinks in structures, a clinician who thinks in patients, a regulatory colleague who thinks in filings. Third, a sense of limit. The candidates who worry me are the ones who treat every positive blot as a drug. The candidates I trust can say what would change their mind.
Bring a short list of techniques you have actually used, named at the level of the skill rather than a procedure. Bring the names of collaborators who will take a call. If a visa, a relocation, or a need to finish a dissertation is on the calendar, say it before the team imagines you at a kickoff next month. Industry moves faster than a thesis committee. Academic searches move slower than industry and then expect a grant strategy. Match the story you tell to the clock of the place you want.
From first author to the person who sets the aim
Early career work is supervised. You join a project someone else framed, you generate results they can trust, and you learn how decisions get made above you. A postdoctoral appointment is common after a doctorate, especially if you want a university faculty path. Industry sometimes hires new doctorates directly into scientist roles and trains them inside a therapeutic area. Neither path is a moral ranking. They teach different skills: one teaches you to build a lab identity, the other teaches you to ship a decision on a calendar.
A hiring team can also tell whether you have sat with messy data. Describe a week when the result contradicted the aim, who you told, and what you proposed next. That story matters more than a list of instrument names. If the role is in a company, ask which therapeutic area the seat actually serves and whether you will see the study from design through the decision meeting. If the role is academic, ask what you must teach and what portion of the year is protected for the research. Those answers change the job as much as the title on the offer letter.
Mid-career, the job shifts from doing every measurement to designing the study and reviewing other people's work. You may lead a small group, sit on a development team, or own a slice of a drug's nonclinical story. Titles vary: senior scientist, group leader, research assistant professor. The substance is that other people now depend on your judgment. You spend more time on aims, budgets at a planning level, and the choice of which experiment is worth the animals, the samples, or the months. You still need enough proximity to the data to smell a weak figure.
Later forks include a broader scientific leadership role, a specialist reputation in one therapeutic area, or a move toward regulatory science, medical writing grounded in data, or alliance work with outside labs. Some people leave the bench and miss it. Some people stay individual contributors on purpose because the science is why they came. When you compare offers, compare the scientific scope and the support: who analyzes data with you, whether you can publish, and whether "scientist" means a real project or a permanent seat in someone else's meeting. Pay matters, and so does the chance to keep learning a field that moves.
Pay inside a wider scientific series
These wages come from Occupational Employment and Wage Statistics, May 2025. The Bureau publishes them for Medical Scientists, Except Epidemiologists, a wider series than the single title pharmacologist, and this is the one place that wider name belongs. The entry figure is $64,800. The national median is $103,410. The gap from entry to the median is $38,610. Early research roles and trainee-adjacent pay can sit nearer the entry figure. A scientist with a finished doctorate and a real project should look hard at any offer that treats $64,800 as the center of the market when the national median is $103,410.
The high end of the published range in Kansas is $219,100. That high end differs from a state median. The gap from the national median to that Kansas high end is $115,690. Quoting $219,100 as if it were typical pay misreads the chart. The highest median is in California, at $136,990, which is $33,580 above the national median. California leads on typical pay. Kansas is where the published range reaches its high end. Those are different facts about different statistics, and they should stay in different sentences.
Other state medians worth holding: New Jersey at $129,280, Massachusetts at $128,210, Connecticut at $116,990, and Arizona at $116,820. The lowest median in the published set is Oklahoma, at $76,480. The gap between California's median and Oklahoma's median is $60,510. If you are choosing between a coastal lab and a lower-median state, the chart gives you a public comparison. It does not price your equity, your grant summer salary, or a bonus plan. Those sit beside the Bureau figures. They do not replace them.
Using the figures when an offer arrives
Put four numbers on a single note before you answer: the base they offered, the national median of $103,410, the entry figure of $64,800 if they are calling you entry-level, and the state median if your state is listed here. A California offer should be read against $136,990, not against the Kansas range top of $219,100. A New Jersey offer belongs next to $129,280. A Massachusetts offer belongs next to $128,210. Connecticut's median is $116,990. Arizona's is $116,820. If your state is absent from this short list, stay with the national median and say so, rather than borrowing another state's high end.
The $38,610 gap from entry to median is the spread you can point to when a first industry role is priced like a stipend. The $33,580 gap from the national median to California's median is the spread you can point to when a recruiter says location does not matter. The $115,690 gap up to the Kansas high end is a warning label: that figure is a range top, and it differs from every median on this page, including California's $136,990. Oklahoma's $76,480 is the low median, $60,510 under California, and it should not be used to anchor a job in a higher-median state.
Then talk about the work the base is buying. A lower base with a real project, your name on the science, and a path to a senior scientist title can beat a higher base in a role that is only meetings. A high base with no support staff can be a lonely bench. Ask what "scientist" means in that group, who reviews your study plans, and whether publication is allowed. Keep the wage labels honest while you ask. You are a pharmacologist negotiating inside a broader published series, and the clearest sentence you can say is the one that names the median, names the state if you have it, and refuses to treat a range top as a promise.
The top of Pharmacologist pay — and how to get there with AI
$219,100what Pharmacologist pay reaches in Kansas
Highest state-level top-of-range annual wage for Medical Scientists, Except Epidemiologists, 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 — Biochemists and Biophysicists — reaches $222,850 in Texas.
$64,800entry$103,410middle$219,100top end
Publication records look similar across this profession; the pharmacologist paid at the top of the range is the one whose dose-response numbers hold up in another laboratory, because assay validation and analysis are systems that person built rather than habits nobody wrote down.
Evaluating the effects of drugs, gases, pesticides, parasites and microorganisms at different levels, preparing and analysing organ, tissue and cell samples for toxicity, and directing studies of disease and treatment all produce numbers that somebody downstream will bet money on. Yet the assay itself is usually a protocol passed hand to hand, with plate layout, control placement and outlier handling decided differently by each person at the bench. That is where results quietly stop replicating. Writing a validated analysis pipeline — fixed plate maps, automatic control checks, curve fitting that refuses to silently drop points — used to compete with grant writing for time. With an assistant drafting the code and IBM SPSS Statistics or a scripted workflow doing the fitting, it is a few weeks, and the scientist who owns it becomes the one collaborators route their compounds through.
Your playbook, by where you are now
Just startingLearn the bench, then distrust it
Run the same assay ten times and record the variation before you believe any single result from it.
Follow the safety procedures for toxic materials exactly, and treat contamination control as part of the data quality, because it is.
Keep a laboratory record detailed enough that another person could repeat your cell or tissue preparation without asking you a question.
Learn enough Python or scripting to fit your own curves rather than pasting numbers into someone else's template.
Read the methods sections of the papers you rely on and note every parameter they failed to state.
What proves it: A repeatability study of your core assay with its variation quantified.
Realistic span: your doctoral years and first postdoctoral post
A few years inBuild the pipeline the group runs on
Fix the plate layout, control placement and acceptance criteria for your group's main assay, and write them down as rules rather than preferences.
Automate the path from instrument output to fitted parameters so nobody transcribes a number by hand again.
Have Claude draft the analysis and quality-check code, then read every line and test it against a dataset whose answer you already know.
Store study data in a structured system such as FileMaker Pro or a shared database instead of in files named after dates.
Publish your validation as a methods paper, since a documented assay is cited by people who will later want to collaborate.
What proves it: A validated assay and analysis pipeline other members of the group use unchanged.
Realistic span: years three through seven
ExperiencedOwn the standard the programme depends on
Take responsibility for how studies investigating disease, prevention and treatment are designed, so sample size and controls stop being negotiated per project.
Write the quality section of grant applications yourself, because reviewers increasingly fund methods rather than hypotheses.
Teach the analysis and laboratory procedures to physicians, residents, students and technicians as a formal course, not as corridor advice.
Present findings to non-specialist audiences with the uncertainty intact, which is rarer and more valued than it sounds.
Look at where this expertise is priced; Kansas leads for medical scientists, and biochemistry and biophysics roles reward the same rigour.
What proves it: A programme-wide analytical standard, funded work that cites it, and trained staff who follow it.
Realistic span: eight years and onward
The next 90 days
Take your group's most-used assay and, over ninety days, find out how reproducible it really is. Have three people run it independently on the same material, blind to each other's results, and compare the fitted parameters. Then run one operator's version across three separate days. The spread you get is the honest error bar on everything your group has published using that assay, and it is very often wider than the differences people are drawing conclusions from. Write it up internally with a proposed fix: a fixed plate map, mandatory controls on every plate, a written rule for excluding a point, and an automated fit so nobody adjusts a curve by eye. Nobody enjoys receiving this document. But a pharmacologist who produces it becomes the person whose name on a dataset means something specific.
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 in the modeling software the field runs on. If you touch PK/PD, open Certara Phoenix (WinNonlin) for non-compartmental and population PK, and Simcyp for physiologically based PK; these are where a pharmacologist's predictions carry weight with programs and regulators. Learn their scripting and automation to run more scenarios per study.
Add a general assistant: use Claude or ChatGPT to write R for NONMEM and nlmixr2 workflows, wrangle PK data, and explain a mechanism or guideline, and NotebookLM to interrogate a stack of pharmacology papers. Keep any identifiable clinical data inside validated, compliant systems — never in a consumer chatbot.
The one rule, forever: Predictive pharmacology (ADMET, PBPK, QSAR) is model-based and can be confidently wrong — a mispredicted human dose or a missed drug-drug interaction can harm trial subjects. Every in-silico prediction must be qualified against experimental and clinical data, its assumptions documented, and the model verified per regulatory expectations for model-informed drug development before it informs a dose or a safety decision. Never enter identifiable clinical-trial or patient data into consumer AI tools; use validated, compliant software for regulated work.
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
Model PK/PD and simulate dosing before the clinic
Why this pays: The pharmacologist whose models set first-in-human and Phase 2 doses shapes the most expensive decisions in drug development — high-value, senior work.
Certara Phoenix (WinNonlin)NONMEMSimcyp
1
Run non-compartmental and population PK in Certara Phoenix or NONMEM, and use Simcyp PBPK to project human PK and special populations, documenting every assumption.
2
Reason through a first-in-human dose projection.
Copy-paste this prompt
Act as a pharmacometrician. I'm projecting a first-in-human dose for [compound class] from preclinical data [species, PK parameters, potency]. Walk through the approaches (allometric scaling vs PBPK), the assumptions each makes, how to set the starting dose and safety margins (MABEL vs NOAEL), and the key uncertainties I must sensitivity-test. Note what the first-in-human package should show regulators. General methodology only, no confidential data.
AI frames the methodology; the dose and its justification must come from qualified models and your judgment, defensible to an IND reviewer.
3
Build the exposure-response story and pressure-test the dose across scenarios before it's proposed.
What you'll haveDefensible, model-based dose recommendations — the high-stakes work that earns senior pharmacometric pay.
2
Predict ADMET and drug-drug interactions to kill bad compounds fast
Why this pays: Killing a doomed compound early, or flagging a dangerous interaction, saves programs millions and reputations — value that gets a pharmacologist promoted.
Screen candidates for ADMET liabilities in ADMET Predictor or Schrodinger, and model likely interactions (for example CYP inhibition or induction) in Simcyp before committing to a molecule.
2
Summarize and prioritize the ADMET and DDI risks.
Copy-paste this prompt
You are a DMPK and pharmacology expert. For [compound] with these properties [logP, solubility, metabolism data, CYP data], summarize the ADMET and drug-drug-interaction risks: likely metabolic soft spots, clearance route, transporter and CYP liabilities, and which interactions could be clinically significant. Recommend the experiments to confirm each predicted risk and the order to prioritize them. Flag predictions that are low-confidence.
In-silico ADMET is a triage filter, not truth — confirm liabilities with in-vitro or in-vivo assays before killing or advancing a compound.
3
Route the confirmed risks into the experimental plan and the go/no-go decision.
What you'll haveBad compounds killed early and interactions caught — the risk judgment that saves programs and earns advancement.
3
Mine the pharmacology and safety literature at scale
Why this pays: Comprehensive, fast mechanism and safety reviews sharpen every decision and land you on grants, INDs, and publications — the reputation that drives comp.
ElicitNotebookLMConsensus
1
Use Elicit or Consensus to assemble the evidence on a target or safety signal across many papers, then load the key PDFs into NotebookLM for grounded, cross-paper Q&A.
2
Extract a mechanism-of-toxicity hypothesis with citations.
Copy-paste this prompt
Act as a pharmacology reviewer. For [drug/target] and the safety signal [e.g., QT prolongation], extract from the papers I provide: reported mechanisms, dose and exposure relationships, species differences, conflicting findings and their likely reasons, and gaps. Give me a mechanism-of-toxicity hypothesis and the experiment that would test it. Cite every claim; say 'not in sources' rather than infer.
Grounded tools still miscite — open each cited paper and confirm before a claim enters an IND, label, or publication.
3
Turn the synthesis into a mechanism and safety brief with a traceable citation trail.
What you'll haveFaster, deeper reviews with an audit trail — the input to funded, published, regulator-ready work.
4
Automate PK/PD data analysis and reporting in R
Why this pays: Automating the analysis-to-report pipeline lets a pharmacologist run more studies and turn them around faster — throughput that scales your value.
RClaudeCertara Phoenix
1
Use Claude or Cursor to write R (nlmixr2, ggplot2) that cleans PK data, runs the analysis, and generates the tables, figures, and a draft report section.
2
Generate a reusable NCA analysis script.
Copy-paste this prompt
Write R code to take a standard PK dataset (subject, time, concentration, dose) and produce: non-compartmental parameters (Cmax, Tmax, AUC0-t, AUC0-inf, t1/2) per subject with summary statistics, concentration-time plots (linear and semi-log) by dose group, and a formatted results table. Flag BLQ handling and any subjects with poor terminal-phase fits for manual review. Comment it for a pharmacologist.
BLQ handling and terminal-slope selection change the numbers — review flagged fits by hand; never ship AI-generated PK parameters unchecked.
3
Standardize the pipeline so every study runs the same, reviewed way.
What you'll haveFaster, consistent PK analysis and reporting — the throughput that lets you own more studies.
5
Support regulatory strategy with model-informed drug development
Why this pays: The pharmacologist who can build and defend model-informed arguments to the FDA or EMA is rare and highly paid — the expertise behind principal-scientist roles.
SimcypCertara PhoenixClaude
1
Frame where modeling can replace or de-risk a study — pediatric dosing, DDI waivers, organ-impairment labeling — using Simcyp and Certara Phoenix, and prepare the justification package.
2
Outline the MIDD argument a regulator expects.
Copy-paste this prompt
You are a regulatory pharmacology strategist. For [drug] I want to use PBPK/PK modeling to support [a pediatric dose / a DDI waiver / organ-impairment labeling]. Outline the model-informed drug development argument regulators expect: what data qualifies the model, the verification and sensitivity analyses required, precedents, and the risks of the agency rejecting the approach. Draft the outline of the justification section.
MIDD acceptance hinges on model qualification — align with current FDA/EMA guidance and have the model independently reviewed before you rely on it.
3
Assemble the qualification evidence and the report, and defend it in the regulatory strategy.
What you'll haveModeling that stands up to regulators and replaces studies — the rare skill behind principal-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 $219,100 tier.
Month 1
Automate one PK analysis in R with AI, documenting assumptions as you go.
Months 2-3
Add PBPK simulation (Simcyp) and ADMET or DDI screening to your workflow, validated against data.
Months 3-6
Do a full literature and safety synthesis with a citation trail, and build an exposure-response story.
Months 6-12
Lead the modeling for a dose or go/no-go decision and standardize your analysis pipeline.
Year 2
Own a model-informed drug development argument to regulators — the principal-scientist scope behind top pay.
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 Bolstad 7th already on gis-analyst / urban-planner / forest-ranger / cartographer / hydrologist / park-ranger / archaeologist / wildlife-biologist / paleontologist / city-planner / biologist / conservation-officer / environmental-consultant / limnologist / landscape-designer / urban-forester / ornithologist / seismologist / volcanologist / petroleum-geologist / meteorologist / toxicologist / zoning-inspector / composting-specialist / animal-control-officer (ASIN 0971764751). This leftover page is BLS Medical Scientists, Except Epidemiologists (SOC 19-1042); play 1 is Model PK/PD and simulate dosing before the clinic; play 4 is Automate PK/PD data analysis and reporting in R; start-here is Start in the modeling software the field runs on; Month 1 is Automate one PK analysis in R with AI, documenting assumptions as you go. Spatial / mapping fundamentals text for leftover modeling / exposure / analysis work — not leftover GISP as a card. Confirm 0971764751. Live page HTTP 200, no PC_GEAR / amazon.com/dp / tag=paycrunch-20 at 2026-09-18 3:10 AM PT. Source page: park-ranger.
Next steps for a Pharmacologist
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.
Pharmacologist work is specific enough that a stamped 'check out these courses' block would be noise. BLS files this work as Medical Scientists, Except Epidemiologists (SOC 19-1042). 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 Biology and Medicine and Dentistry; the links search those subjects, not a generic 'career courses' list.
Pharmacologists in this dataset list ESRI ArcGIS software among the tools in use, so a program that names that stack is a better fit than a survey course.
FlexJobs screens remote, hybrid, freelance, and flexible listings so you are not wading through unverified ads. This is a job-board search for Pharmacologist work, not a claim that they list a counted SOC 19-1042 inventory.
Write a Pharmacologist resume, or one aimed at Biochemists and Biophysicists, instead of a blank template. Resume Now is a resume builder; we are not claiming a counted template set for this SOC.
A Pharmacologist resume that names the actual tasks on this page, or the step-up title Biochemists and Biophysicists, beats a blank template when you apply.
What Pharmacologists earn by state
These are the Bureau of Labor Statistics’ own figures for Medical Scientists, Except Epidemiologists, 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.
California
$136,990
highest of them · +32% vs the national median
Oklahoma
$76,480
lowest of the 32 states and D.C. that qualify · -26% vs the national median
The same job pays $60,510 more a year at the median in California than in Oklahoma — 79% higher. That gap is what the Bureau measured, before any question of what it costs to live in either place. The top-of-range figure quoted at the head of this page, $219,100, is a different statistic in a different place: it is the 90th-percentile wage in Kansas. The state that pays the typical worker most and the state where the best-paid go highest are not always the same one.
Source: U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025, SOC 19-1042. 32 states and D.C. 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.
No — someone must design the studies, judge whether a model's assumptions hold, and defend a dose to an IND reviewer who can halt a trial. AI predicts ADMET, simulates PK, and mines literature; it can't own drug safety. Pharmacologists who master modeling and AI shape the biggest decisions; those who don't are limited to running assays.
Can I trust ADMET or PBPK predictions?
As qualified models, not truth. Predictions fail outside their training and validation space, and a wrong human dose can harm subjects. Confirm with experiments and clinical data, document assumptions, and qualify models to regulatory standards before relying on them.
Is it safe to use ChatGPT with trial data?
Not with identifiable clinical or patient data. Use consumer AI for methodology, code, and public-literature work; keep regulated analyses in validated, compliant software and identifiable data in secure systems. Check your SOPs and data-use rules.
How does AI raise my pay?
By putting you on the high-value decisions — first-in-human doses, go/no-go calls, DDI and safety risk, model-informed arguments to regulators — and by scaling your study throughput. Owning model-based decisions that move programs is what earns senior and principal comp.
Which skill compounds most?
Pharmacometrics and modeling, plus the R and AI tooling around it. It's scarce, it sits on the most expensive decisions in development, and AI makes a skilled modeler dramatically more productive.
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.