EXPOSED
This is a catch-all bucket — biosecurity analysts, ecological researchers, lab-based bioinformatics and regulatory science staff — and the modal worker splits time between bench or field protocols and screen work: literature synthesis, dataset cleaning, statistical runs, manuscript and grant drafting. The screen half is exactly what LLMs and AutoML now do at usable quality, which compresses the analyst tier hard. What persists is hands-on assay execution, field sampling in messy conditions, and deciding which experiment is actually worth running and whether a result is real.
Headcount grew steadily across the period.
Median pay $75,910 → $93,750 -1.2% in real terms
This line is counted by the Bureau of Labor Statistics — the one figure on this page that isn't a judgement of ours. Headcount moves on demand, offshoring, demographics and the business cycle, and automation is one term among several, often not the loudest.
So a falling line is not evidence that AI did it, and a rising one is not evidence that it won't. Both happen in this register: some occupations resist automation and shrink anyway, others are highly automatable and keep growing. The marked year is 2020.
BLS projection, 2024–2034
+3.7% 7,800 → 8,100 on the projections basis
Exposed, but growing
AI can already do a lot of these tasks, and the BLS still expects +3.7% more of these jobs by 2034. Demand for the output is growing faster than the work is being automated away — the mechanism BLS gives for software developers, and the combination people most often misread as an error.
Different clocks. The score is what current AI could do to this work today. The projection is how many of these jobs will exist in 2034. Everything between the two — how fast employers actually adopt, whether demand grows in the meantime — is why they can point opposite ways without either being wrong.
~400 openings a year on average, including replacing people who leave.
OsteologistEmbryologistMorphologistPhysiologistPaleobotanistOlericulturistPhytopathologistPlant TaxonomistForensic ScientistLife Science TaxonomistPublic Health MicrobiologistCollector of Aquarium Specimens
The BLS uses Life Scientists, All Other for work that doesn't fit any named occupation, so it covers roles that have little in common with each other. Two consequences worth knowing before you read anything below:
Holding it up: judgment & accountability . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Sequence QC, differential-expression pipelines, literature triage for a review, and first-draft methods sections are already handed to tooling in most labs in this bucket, but designing an assay that discriminates between two competing mechanisms, troubleshooting why the western blot failed on the third replicate, and running a field transect still take a person — a roughly even split, which is why this sits at 10 rather than in the resistant band.
Some physical or field component Bench work here is pipetting, culture maintenance, dissection, and instrument runs in a controlled BSL-1/2 lab, with periodic field sampling — soil cores, water grabs, trapping — that is genuinely uncontrolled but seasonal rather than daily, so it lands above pure desk science and below the year-round outdoor roles that score 15+.
No licence, no signature requirement No state licence gates the title: you can run this work with a PhD or a master's and nobody countersigns your results, and the constraints that do exist — IRB and IACUC approval, select-agent registration, IBC review — attach to the institution and the protocol, not to your name, so the 4 reflects committee oversight rather than personal legal exposure.
Meaningful discretion You decide when a surprising result is an artefact versus a finding, whether the effect size justifies another year of sampling, and how to word an uncertainty caveat that a regulator or a biosecurity customer will act on — real discretion with real consequences, but almost always filtered through PIs, co-authors, and reviewers before it binds anyone, which caps it at 12.
The verdict above describes this occupation as a whole. Almost nobody does the typical version of a job — tick what's actually in your week and see how your own mix sits.
Your task mix speaks to task resistance (10/20 here) — how much of the day's work current AI already does. That is the dimension the boxes above are about.
It cannot move the other three. Liability shield (4/20) is whether the law requires a licensed human to sign. Trust premium (6/20) is whether buyers specifically pay for a person. Judgment and accountability (12/20) is whether the role exists to own consequential calls. Those are facts about the occupation's standing, not about which tasks are in your week — a paralegal who does only trial exhibits still holds no licence. Together they are 22 of this occupation's 43 points (51%).
Embodiment (11/20) is also a property of the work rather than the worker, but we don't tag individual tasks as physical or not, so the picker can't tell you anything about it. That's a limit of this tool, not a claim.
Did we get the list right? Tell us what's missing — the tasks are written from the outside, and you're reading this from the inside.
The moves above are yours to make. This is the other half: what would have to change in the world for the occupation itself to score higher. None of it is in any one person's gift, but it is where the floor actually comes from. Scores here are not a one-way ratchet. Only two of the five dimensions — task resistance and embodiment — track what machines can do. The other three track law, what buyers will pay for, and who is answerable, and those move in both directions, often in response to the same pressure AI creates. If every lever below landed, this occupation would score around 57/100, still EXPOSED.
Task-mix shift: once literature synthesis, data cleaning and first-draft manuscript work are routinely AI-generated, the remaining defensible core is experimental design, assay troubleshooting and adjudicating whether an anomalous result is real. This bucket genuinely has two tiers, and the residual tier is design-and-verify. Watch for journal/funder policies (Nature, NIH) that require named human attestation of data provenance and analysis choices, which pins verification work to a person.
Formal designation of role-holders as accountable persons in regulated regimes — GLP Study Director under 40 CFR 160/792, Responsible Official under the CDC/APHIS Select Agent Program, or biosafety officer sign-off on dual-use research of concern reviews. These are named-individual consequential calls under ambiguity and already exist; expansion of who must hold them raises the score.
Biosecurity screening is the live route: if the Executive-Order-derived nucleic-acid synthesis screening framework (OSTP/ASPR guidance, and the International Gene Synthesis Consortium protocols) hardens into a rule requiring a named, qualified human reviewer to sign off on flagged sequence-order hits and personally attest, that creates a statutory human signature where none exists today. Parallel route: EPA/FIFRA and FDA GLP study-director attestation being extended to require the study director to personally certify AI-assisted analyses.
Growth in field-heavy subspecialties — eDNA sampling, invasive-species and wildlife-disease surveillance under USDA APHIS/USGS contracts, and BSL-3 containment work where remote or robotic handling is not permitted by the institutional biosafety committee — shifts the modal day toward unstructured outdoor and glovebox work.
The limit. Trust premium has no realistic route: the buyers are grant panels, agencies and contract sponsors who purchase results and compliance, not human authorship, and no client segment pays extra for a human ecologist or bioinformatician as such. Also note the shield levers apply unevenly — a select-agent Responsible Official gains a lot, a soft-money ecology postdoc in the same SOC bucket gains nothing, so the bucket average moves far less than any individual subspecialty.
| Durham-Chapel Hill, NC | 460 | $105,850 +13% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 340 | $126,120 +35% |
| New Orleans-Metairie, LA | 270 | $60,200 -36% |
| Houston-Pasadena-The Woodlands, TX | 230 | $61,440 -34% |
| Los Angeles-Long Beach-Anaheim, CA | 230 | $104,740 +12% |
| New York-Newark-Jersey City, NY-NJ | 220 | $100,600 +7% |
| Richmond, VA | 210 | $70,050 -25% |
| Baltimore-Columbia-Towson, MD | 170 | $105,900 +13% |
| San Francisco-Oakland-Fremont, CA | 170 | $134,810 +44% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 340 | $126,120 +35% |
| San Jose-Sunnyvale-Santa Clara, CA | 60 | $125,430 +34% |
We have no reported case of a named organisation automating this occupation. Not one deployment, not one announcement.
That is worth saying out loud next to a score of 43. The verdict above is about what the work exposes — what current AI could do to these tasks. It is not a claim that anyone has done it. For this occupation those two things have come apart completely: the capability argument is on this page, and the evidence column is empty.
Has AI actually changed your work? One tap, anonymous, and the running tally is public. Nothing else is asked of you.
Rather than check back: get the digest and we'll tell you what changed — or watch a single occupation from its own page.