Become the Biostatistician the whole group asks first
$213,610top of the range in Michigan · middle $105,650 / yr
AI augments this role
Biostatisticians in the United States earn a median of $105,650 a year. Pay starts near $64,000. Pay reaches $213,610 at the top of the range in Michigan, 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 (Statisticians, SOC 15-2041). Last checked 9 September 2026.
Entry level
$64,000
Top of the range · Michigan
$213,610
Education
Master's degree in Biostatistics
Wages — U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025 (Statisticians). 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 BiostatisticianReviewed September 2026
We track new AI-tool launches every week and refresh this list — here’s what’s gaining traction for Biostatistician work right now.
Julius AINEWFree / $20 mo
AI data analyst that runs statistics and charts from plain-language prompts.
How a Biostatistician 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 Biostatistician 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 Biostatistician 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 Biostatistician uses it: get evidence-backed answers with the studies behind them
SciSpaceFree / paid
AI that explains papers and helps with literature review.
How a Biostatistician 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 Biostatistician 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 Biostatistician 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 Biostatistician 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 Biostatistician uses it: draft and reply inside Google Workspace and research without leaving the page
Nobody has enrolled yet, and the biostatistician is already marking up the protocol. The clinic hopes to learn whether a treatment helps. The comparison, as written, mixes two kinds of patients and calls them one group. The statistician redraws the groups, names the outcome the study will actually chase, and tells the physician-investigator what the design can support before the first person signs a consent. Later, when the data exist, the same person will analyze them and refuse a sentence the numbers cannot carry. Designing the study and analyzing the health data are one occupation, not two errands stapled together.
Redrawing the comparison before anyone enrolls
A biostatistician designs studies and analyzes health data. The early work is the protocol: who is compared, what is measured, how people are assigned, what will be done about missing information, and which analysis will answer the aim. The later work is the data themselves: cleaning that you can explain, the planned analysis, the extra look you promised not to treat as proof, and a written result a clinician can read without being misled. You are the person who says both "this design can answer that" and "this result does not stretch as far as the abstract hopes." Teams that skip you at the start pay for it at the end, when the dataset cannot support the claim they already promised a funder or a regulator.
The week puts you with physician-investigators, epidemiologists, data managers, programmers, and medical writers. In a company you also sit with clinicians who run the trial and with colleagues who prepare submissions. In a university or a hospital you sit on study teams and sometimes advise many investigators who each believe their project is simple. Tools are statistical software such as R or SAS, an analysis plan, shells for tables, and a record of every decision that changed a number. Places include pharmaceutical and biotech companies, contract research groups, medical schools, cancer centers, public-health agencies, and insurers or health systems that study their own outcomes. The decision you own is what the study is allowed to say.
Design conversations are concrete. How will you stop a trial if continuing would be wrong. How will you keep the comparison fair when a site enrolls differently from the others. What happens if the outcome is late and the sponsor is impatient. You also decide what not to add. A protocol stuffed with extra aims becomes a study that answers none of them well. Analysis conversations are just as concrete. Which records fail a check. Which participants sit outside the group you defined. What a sensitivity look changes, and whether that change is large enough to soften the sentence. You write it down so a reviewer who was not in the room can follow you.
Health data are not only trials. Some biostatisticians spend their time on observational studies, registries, and the records a health system already holds. The craft shifts from assignment to careful comparison when assignment was never in your control. The ethic of the job stays. You do not dress a biased comparison as a treatment effect because a stakeholder prefers that story. If your background is theoretical statistics with no interest in illness, missing data, or a protocol, a general statistics seat may fit you better. If you want the study team, the clinic's constraint is part of the attraction, not a nuisance.
Statisticians, the series this page borrows
The pay figures are the Bureau of Labor Statistics series Statisticians, SOC 15-2041, from Occupational Employment and Wage Statistics in May 2025. Biostatisticians are a health-focused part of a broader statistician workforce, and this page uses that broader series for the dollars. Say so once. A hiring manager in a trial group will understand you are quoting the statistician series because that is the published title tied to these wages. The facts here do not include an employment headcount, so leave size-of-occupation talk alone and negotiate with the wages. Do not paste these dollars onto a software-engineering offer or onto a clinical licence profession. The match is study design and health-data analysis.
A master's or a doctorate, and nothing to hang on the wall from a state
The degree is the door
A master's or a doctorate in biostatistics or statistics is the usual preparation. No licence covers the work. Employers look for study designs and analyses a clinician or a reviewer can follow, produced under someone who already knows the craft.
A master's is a common door onto a study team as a statistician who analyzes under a senior person and gradually takes more of the design. Principal biostatisticians, academic posts, and roles that set method for other statisticians usually call for a doctorate. Some companies hire strong master's graduates into senior responsibility over time without a doctorate, when the portfolio of trials is deep. Read the posting. A posting that requires a doctorate means that requirement. A posting that accepts a master's does not ask you to wait for a further degree. Undergraduate statistics or a quantitative science can start you toward the master's. It rarely stands alone for an independent seat on a trial.
Preparation is the degree, supervised work on real studies, and a portfolio that shows judgment. A thesis, a methods section you wrote, or a de-identified analysis plan is enough to discuss. Walk through the aim, the comparison, the analysis, and a limitation. Faculty and senior biostatisticians listen for whether you protected the claim. They have all met someone who could run software and could not say what the output meant for a patient or a protocol. Programming skill matters. It is the tool. The product is the decision about the study. Employer training will add the company's data handling, the therapeutic area, and the way a submission is assembled. Arrive ready to learn those. Arrive already able to argue a design.
Professional societies in statistics offer optional credentials and courses. They can sharpen a specialty. They are not a licence, and most study teams will hire on the degree and the studies long before they ask for an extra card. If you mention one, keep it beside a protocol you can defend. Skip any tour of how an exam is built. The collaborator across the table wants to know if you will tell them no when the design is weak. That willingness, taught in supervised practice, is the scarce habit.
Joining a study team
Drug and device companies, academic medical centers, contract research organizations, and public-health groups hire. Apply with a therapeutic area or a study type you have touched: oncology trials, observational health records, device studies, public-health surveillance analysis, or another corner you can discuss. Name the software you can use without a manual beside you. Name one design choice you influenced. A transcript full of course titles and no study is a student record. A single protocol you can explain turns it into a hiring record.
Ask who signs the analysis plan and who speaks to the investigator when the design must change. A junior statistician under a principal who still reviews the claim will learn the craft. A junior statistician left alone with a dozen investigators and no review will learn to appease. Ask how many studies you would touch at once, and whether programming support exists or whether you are also the programmer. Both models exist. The pay and the week should match the model. Ask what a good first year looks like: clean deliveries, a protocol the team did not have to rebuild, a reputation for being early rather than heroic at the end.
References should include a statistician who has read your work and, if you can, an investigator who used it. The statistician can speak to whether your code and your reasoning agree. The investigator can speak to whether you communicated a limit without contempt. In the interview, expect a small design problem on a board. State assumptions out loud. Say what you would refuse to conclude. If a clinical fact is unfamiliar, ask how the clinic defines it, rather than inventing a medical fact. Biostatisticians are hired for discipline. Bluffing is the fastest way to look undisciplined.
Statistician, senior, principal
The path on a study team runs from statistician to senior statistician to principal biostatistician. A statistician carries analyses, helps shape a protocol, and checks the work against the plan. A senior statistician owns studies, reviews other statisticians, and is the person an investigator calls when the design is in trouble. A principal biostatistician sets method for a group or a therapeutic area, hires, and is accountable for the claims that leave the organization. Academic titles use different words for a similar climb, from analyst or research statistician toward a faculty or core-lead role. Watch who is allowed to say the final sentence about the result.
You move up when other people trust your no. The statistician who catches a broken comparison before enrollment, and who writes the fix so the team can follow it, is already practicing senior judgment. The senior who can teach that habit, and who can sit with a regulator's concern or a manuscript dispute without folding, is the one considered for principal. Keep a list of studies, your role, the design point you changed, and the claim you stood behind or refused. Confidential details stay out of the resume. The list still shows scope. If every line says "ran the tables," you are on the statistician rung, and the offer should be read there until design ownership is real. A master's can carry you far on this ladder. A doctorate is the common key to the principal chair and to many academic ones. Match the degree to the chair you are actually seeking.
Michigan's high end and the District's typical pay
The entry figure is $64,000. The national median is $105,650. The gap between those two is $41,650. The Michigan top figure reads $213,610, shown where the Bureau had enough statisticians to release it. The distance from the country's middle up to that Michigan top is $107,960. The District of Columbia median, typical pay there, is $140,670, and the District median runs $35,020 higher than the national figure. New York lists a median of $136,020. California's median is $135,960. Maryland's median is $132,620. New Jersey's median is $118,280. Michigan is the high-end place and does not appear on the median list. The District leads the medians that do appear. $140,670 and $213,610 answer different ideas: typical pay in the District of Columbia, and the high end of the range in Michigan.
A new statistician on a study team, master's in hand and still reviewed on every plan, should set an offer beside $64,000. The $41,650 toward $105,650 is the talk when you already own analyses and influence design, and the letter still pays a new graduate's rate. A senior statistician can anchor on $105,650. If the job is in the District of Columbia and you mean typical pay, use the $35,020 step to $140,670. New York, California, Maryland, and New Jersey each have a median you can cite the same way, as typical pay in that place, without calling any of them the high end. The $107,960 from the national median to $213,610 belongs with principal scope or a scarce senior specialty, at the high end in Michigan. It is a clumsy first number for someone who has not yet signed an analysis plan.
Lead with the rung. Statistician: $64,000, and a clear picture of what crosses $41,650, such as independent study ownership, review of a junior colleague, or primary contact with investigators. Senior: $105,650, then a state or District median only when you are in the District of Columbia, New York, California, Maryland, or New Jersey and you are discussing typical pay. Principal, aimed at Michigan's high end: where the role sits along the $107,960, and what the employer means by principal, whether one therapeutic area, a department, or the statistical voice on submissions. This page gives you no headcount to cite. It gives you entry, the median, Michigan's high end, five medians, and three gaps. Spend those, and stop.
The protocol markup is still the heart of the hire. A master's or a doctorate gets you into the room. No licence is required to stay there. Statistician, senior, then principal biostatistician: the climb is whose judgment the study uses. Place a new-team offer next to $64,000, a senior offer next to $105,650, the District's typical pay next to $140,670 when that is the city and the meaning, and Michigan's $213,610 only as that state's top figure.
The top of Biostatistician pay — and how to get there with AI
$213,610what Biostatistician pay reaches in Michigan
Highest state-level top-of-range annual wage for Statisticians, 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 — Mathematicians — reaches $195,190 nationally.
$64,000entry$105,650middle$213,610top end
Between the middle and the top of this job sits one difference: whether you write good analysis code, or whether your way of writing it is the way the department has adopted.
Statistical groups adopt tooling badly. Somebody tries Python on a trial dataset, it works, and three years later six people have six incompatible ways of producing the same tables. Whoever standardises that becomes structural. This occupation rewards it directly, because so much of the work is other people's: writing detailed analysis plans, reviewing clinical research protocols and recommending appropriate analyses, providing biostatistical consultation to colleagues. Assistants that write code have made standardising urgent — junior staff already generate scripts they cannot fully defend, and somebody has to decide what gets checked before output reaches a data monitoring committee.
Your playbook, by where you are now
Just startingMake your own code reviewable
Put every analysis script into Git, throwaway ones included, so your history is visible.
Write the analysis plan before the code, even for a small request, and keep both in one folder.
Use Claude to explain unfamiliar package output, then confirm it against the documentation and a dataset whose answer you already know.
Rebuild one legacy IBM SPSS Statistics analysis in Python and reconcile the results line by line.
Volunteer for a protocol review and record which analyses you recommended and why.
What proves it: A version-controlled repository where any colleague can rerun one of your analyses and reproduce your numbers.
Realistic span: the first two years
A few years inTurn your habits into the group's habits
Write the template the group uses for tables and graphs presenting clinical data, so figures stop being rebuilt for every study.
Run a short internal session on the tool people already use badly, such as branching in Git or checking assistant-written code against a known result.
Set the rule for what a script must show before its output goes into a report for a monitoring committee.
Answer the consultation requests nobody answers, in writing, so the answers accumulate somewhere.
Teach a seminar module in biostatistics if your institution runs one, because teaching forces the standard to become explicit.
What proves it: A code and reporting template the department uses, plus a training session you built and ran.
Realistic span: roughly years three to seven
ExperiencedHold the standard against the regulator
Define what evidence an analysis must carry before it supports anything sent to a federal regulatory agency.
Review the work of the people you trained instead of only running your own analyses.
Take the tooling decision into the room where research proposals and budgets are written.
Write the onboarding path so a new statistician is productive without a month of shadowing.
What proves it: A written analysis standard your organisation follows and a group trained to it.
Realistic span: year eight onward
The next 90 days
Choose the single task your colleagues repeat most — usually producing the tables and graphs that go into a report — and spend ninety days making one shared version. Write the code once, in whatever language the group actually runs, put it in Git, and document the three decisions inside it a reviewer would question. Then sit with two colleagues, one at a time, and watch them use it. Their confusion shows what your documentation lacks. Fix that, then offer a lunchtime walkthrough to everyone else. A biostatistician who does this stops being one of several people who could do the analysis and becomes the one whose version the rest run.
Wage figures: BLS OEWS, May 2025. The playbook is PayCrunch editorial guidance, not a guarantee of pay or placement.
Every figure is the national median from the U.S. Bureau of Labor Statistics (OEWS) shown on that role’s own page.
Never used AI before? Start here (2 minutes).
Start by wiring an AI coding assistant into the environment you already use. If you write R, run Posit (RStudio/Positron) with its built-in assistant or GitHub Copilot; if you write SAS, use SAS Viya Copilot. Ask it to draft a function or macro, then read and validate every line -- you own correctness.
For method questions and SAP prose, open Claude or ChatGPT (with code interpreter) -- they're strong at explaining a statistical approach, drafting analysis-plan boilerplate, and sanity-checking simulation code. Keep all real, patient-level or unblinded data inside your validated systems; use general tools only with synthetic or fully de-identified aggregate data.
The one rule, forever: Statistical code from an AI is a draft, not a result -- every analysis must be independently validated (double-programming/QC) and traceable to your SAP before it touches a regulatory submission or a publication. Never paste identifiable patient-level trial data or unblinded results into a consumer AI tool; use only validated, access-controlled environments, and preserve the audit trail and reproducibility your study and 21 CFR Part 11 require.
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
Draft and validate statistical code faster
Why this pays: Programming is where a biostatistician's hours go. Generating a working first draft of a SAS macro or R function -- and using AI to help QC it -- multiplies how many analyses you can deliver, the throughput that gets you onto more (and more pivotal) studies and into principal roles.
In Posit or with GitHub Copilot in your IDE, describe the analysis and let it draft the code; then read every line and run it against known test data before trusting it.
2
Generate CDISC-aware programming with a precise prompt.
Copy-paste this prompt
Write a documented R function (tidyverse) that takes ADaM [ADSL and ADAE] datasets and produces a treatment-emergent adverse-event summary table by system organ class and preferred term, with counts and percentages by treatment arm plus a total column, following [ICH E3] conventions. Include input assumptions, inline comments, and a list of edge cases to test. I will validate against a double-programmed result.
AI drafts; you validate by independent double-programming. Use only synthetic or de-identified structures in the tool, never real subject data.
3
Ask the assistant to write unit tests and edge cases too, then run your own QC. Faster, well-tested code is what lets you carry more studies.
What you'll haveMore validated analyses delivered per month -- the throughput that earns lead-programmer and principal roles paying toward $213,610.
2
Accelerate SAP and protocol authoring
Why this pays: The statistical analysis plan and the design section of the protocol are high-value deliverables that gate every trial. Drafting them faster and more completely frees you for the design thinking that distinguishes a principal from a programmer.
ClaudeChatGPTMicrosoft Copilot
1
Draft SAP boilerplate and structure from your design, then refine.
Copy-paste this prompt
Draft the outline and standard-language sections of a Statistical Analysis Plan for a [randomized, double-blind, placebo-controlled Phase 3] trial with primary endpoint [change from baseline in HbA1c at 24 weeks], analyzed with a [mixed model for repeated measures]. Include sections for estimands (ICH E9(R1)), analysis populations, handling of missing data and intercurrent events, multiplicity control, and sensitivity analyses. Flag every place I must insert study-specific detail.
AI drafts boilerplate; the estimand, design, and every assumption are your professional responsibility. Align to your SOPs and the actual protocol.
2
Use Claude to pressure-test your draft: ask it to argue how an FDA reviewer might critique your missing-data or multiplicity strategy, then strengthen those sections.
What you'll haveComplete, defensible SAPs drafted in a fraction of the time -- capacity to own more trials and the pivotal work that pays top-of-range.
Why this pays: Sample-size and adaptive-design expertise is where biostatisticians add the most value and command the most pay. AI helps you build and check simulations fast so you can offer designs that save your sponsor time and money.
R (with AI assistant)nQueryEast by Cytel
1
Prototype a simulation to justify a design choice.
Copy-paste this prompt
Write R code to run a Monte Carlo simulation estimating power for a [group-sequential Phase 2] design with [two interim analyses using an O'Brien-Fleming boundary], assumed effect size [0.4], and [1:1] allocation. Parameterize n, effect size, and number of simulations; return empirical power and type I error. Comment the code and note the assumptions I should vary in a sensitivity analysis.
Verify the simulation against a closed-form result or nQuery/East before you rely on it. AI accelerates prototyping, not final validation.
2
Confirm the AI-built simulation against nQuery or East by Cytel for the final design, and present the trade-offs (sample size vs. power vs. duration) to your clinical team.
What you'll haveEfficient, well-justified designs that save sponsors time and money -- the consultative value that lifts you into senior, top-paid roles.
4
Automate literature review and meta-analysis
Why this pays: Grants, protocols, and publications all need fast, rigorous evidence synthesis. AI-assisted screening and extraction turns weeks of review into days, freeing you for analysis and making you the person who ships papers and grants.
ElicitConsensusR (metafor)
1
Use Elicit or Consensus to find and screen studies, extracting effect sizes and sample sizes into a structured table you verify against the source papers.
2
Draft the meta-analysis pipeline.
Copy-paste this prompt
Write R code using the metafor package to run a random-effects meta-analysis on this extracted data [study, effect size, variance], produce a forest plot and a funnel plot, compute I-squared for heterogeneity, and run Egger's test for publication bias. Comment each step and tell me how to interpret the heterogeneity and bias diagnostics.
AI drafts the pipeline; you confirm every extracted number against the primary source. Screening still needs human eligibility judgment.
What you'll haveRigorous evidence synthesis in days not weeks -- more publications and funded grants, the record that drives academic and industry pay.
5
Explain the statistics to non-statisticians
Why this pays: Biostatisticians who make a result land with clinicians, regulators, and executives get pulled onto the visible, high-stakes projects. AI helps you translate and visualize without dumbing down.
ChatGPTClaudeJulius AI
1
Turn a dense result into a clear narrative for a specific audience.
Copy-paste this prompt
I need to explain this result to a clinical team without a stats background: [paste the de-identified summary -- e.g., adjusted odds ratio 1.8, 95% CI 1.2-2.7, p=0.006, from a logistic model]. Write a 3-sentence plain-language interpretation, the one caveat they must understand, and a suggestion for the single clearest chart to show it. No jargon, but statistically accurate.
Keep results de-identified and aggregate. You are responsible for statistical accuracy -- never let a simplification become misleading.
2
Use Julius AI or an AI assistant in R to iterate quickly on a publication-quality ggplot2 figure that makes the finding obvious.
What you'll haveFindings that land with decision-makers -- the visibility and trust that get you onto pivotal projects and into leadership pay.
6
Build a reproducible, regulator-ready workflow
Why this pays: Reproducibility and traceability are non-negotiable in regulated research, and a biostatistician who automates them is trusted with submission-critical work. AI helps you build the pipelines, documentation, and QC that make your analyses audit-proof.
GitHub CopilotR (targets/Quarto)SAS Viya
1
Have Copilot help you scaffold a reproducible pipeline (an R {targets} workflow or a documented SAS driver program) under version control, so every table traces from raw data to output.
2
Generate the documentation and QC layer.
Copy-paste this prompt
Help me design a QC and documentation framework for a clinical-trial analysis package: a checklist for double-programming key outputs, a template for a programming specification per table/listing/figure, a reproducibility check (same input -> same output), and a define-style data dictionary structure. Regulatory-grade, aligned to good programming practice.
AI structures the framework; validation and sign-off are human and must meet your SOPs and 21 CFR Part 11. Never automate away the QC.
What you'll haveAudit-proof, reproducible analysis packages -- the reliability that earns submission-critical work and principal-level pay.
Your 12-month sequence to the top of the range
How the plays above stack into a path from median pay toward the $213,610 tier.
Month 1
Wire an AI assistant (Copilot/Posit/SAS Viya) into your IDE. Use it to draft and QC one routine analysis; validate every line against a known result.
Months 2-3
Speed up SAP and table/listing/figure programming with prompt templates; make double-programming AI-drafted code your standard QC.
Months 3-6
Take on design work -- build and validate power and adaptive-design simulations in R against nQuery/East; offer sponsors efficient designs.
Months 6-12
Automate literature synthesis and reproducible pipelines; ship a publication or own a submission-critical deliverable.
Year 2
Move toward principal: own a pivotal-trial SAP end to end and lead QC and standards for your team -- the top-of-range track.
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. This leftover page’s few-years track is Rebuild one legacy IBM SPSS Statistics analysis in Python and the FAQ is Should I learn R, SAS, or Python for AI-augmented work? Play 1 is Draft and validate statistical code faster. Not CompTIA Data+ and not leftover Ross Exam P (that is actuary). Confirm 109810403X. Live page HTTP 200, no PC_GEAR / amazon.com/dp / tag=paycrunch-20 at 2026-09-17 3:31 PM PT.
Next steps for a Biostatistician
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.
Biostatistician work is specific enough that a stamped 'check out these courses' block would be noise. BLS files this work as Statisticians (SOC 15-2041). 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 area is Medicine and Dentistry, which is what the course searches below actually query.
Biostatisticians 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 medicine and dentistry — 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 Biostatistician work, not a claim that they list a counted SOC 15-2041 inventory.
Write a Biostatistician resume, or one aimed at Mathematicians, instead of a blank template. Resume Now is a resume builder; we are not claiming a counted template set for this SOC.
A Biostatistician resume that names the actual tasks on this page, or the step-up title Mathematicians, beats a blank template when you apply.
What Biostatisticians earn by state
These are the Bureau of Labor Statistics’ own figures for Statisticians, 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.
District of Columbia
$140,670
highest of them · +33% vs the national median
Missouri
$66,330
lowest of the 19 states and D.C. that qualify · -37% vs the national median
The same job pays $74,340 more a year at the median in District of Columbia than in Missouri — 112% 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, $213,610, is a different statistic in a different place: it is the 90th-percentile wage in Michigan. 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 15-2041. 19 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. AI can draft code and explain a method, but it routinely produces plausible-looking statistics that are wrong, and it cannot choose the estimand, defend a design to the FDA, or take responsibility for numbers behind a drug approval. That judgment is the job. AI makes the programming and writing faster; the biostatisticians who validate its output and reinvest the time in design and interpretation pull ahead.
Can I trust AI-generated statistical code?
Only as a first draft you fully validate. Treat every AI-written macro or function like code from a junior programmer -- read it, test it on known data, and double-program anything that matters. AI hallucinates functions, misapplies methods, and gets edge cases wrong; your QC is what makes it safe.
Is it safe to use ChatGPT with trial data?
Never with identifiable patient-level or unblinded data. Keep real study data in your validated, access-controlled environment. Use consumer AI only with synthetic data, fully de-identified aggregates, or method and code questions -- and preserve the audit trail your regulated work requires.
How does AI raise a biostatistician's pay?
Pay climbs with the complexity and volume of work you can own. AI multiplies your programming and writing throughput and helps you offer sophisticated designs (adaptive, Bayesian, simulation-based). That lets you carry more studies and pivotal work -- the path from analyst to principal to top-of-range comp.
Should I learn R, SAS, or Python for AI-augmented work?
Keep SAS for regulated pharma submissions (still the FDA-familiar standard) and lean on R or Python for simulation, visualization, and modern workflows -- AI assistants are strong in all three. The differentiator isn't the language; it's your ability to validate AI output and design good studies.
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.