EXPOSED
The modal natural sciences manager splits time between screen work that AI now does well — drafting grant narratives and progress reports, summarizing literature, building budget spreadsheets, reviewing protocols for completeness — and work that resists it: deciding which research lines to fund or kill, hiring and developing scientists, walking the lab floor on safety and compliance, and owning results in front of funders, regulators, and executives. No license is required for the role itself, so the protection is organizational accountability rather than statute. The judgment tier is real and consequential, but the documentation and reporting load that fills much of the week is exactly what large language models compress.
Headcount grew steadily across the period.
Median pay $129,100 → $167,220 +3.6% 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% 104,300 → 108,200 on the projections basis
Growing, and only partly exposed
The BLS expects +3.7% more of these jobs by 2034, and at 53/100 the work is only partly exposed — some tasks are automatable, the core of the job is not. Nothing here is in tension.
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.
~8,500 openings a year on average, including replacing people who leave.
Gravity ManagerHydrotechnicianClinical ManagerResearch ManagerQuarrying ManagerResearch DirectorWater Team LeaderWild Life ManagerGeological ManagerRegulatory ManagerGeochemical ManagerGeophysical ManagerPostdoctoral FellowResource SpecialistClinical CoordinatorResearch CoordinatorWater Resource AgentPower Supply EngineerClinical Study ManagerClinical Trial ManagerPostdoctoral AssociateResearch AdministratorPostdoctoral ResearcherWater Resources Planner
Holding it up: judgment & accountability . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Grant-narrative drafting, quarterly progress reports, literature landscape summaries, and budget rollups are now first-drafted by a model in minutes, but portfolio triage — killing a project because the assay reproducibility is drifting and the PI is defending it too hard — plus headcount decisions and IACUC/IRB negotiation stay manual, which lands this at mixed rather than resistant.
Some physical or field component A working R&D or lab manager still walks the floor for chemical hygiene plan compliance, signs off on hood certifications and waste manifests, tours field sites and pilot plants, and shows funders the bench — but the majority of the week is Teams, Excel, and eLN review, so the physical component is recurring rather than defining.
Certification preferred, not legally required The management title requires no license; what protection exists comes from being the named PI, the responsible official on an EPA or select-agent registration, or the signatory on an institutional assurance — role-attached duty rather than a credential a statute reserves for you, and many holders carry none of it.
Exists to be accountable for ambiguous calls You decide which of six programs gets the next FTE on incomplete preclinical data, whether a safety incident is reportable, whether to disclose an anomaly to a sponsor before the readout, and whether a scientist's finding is solid enough to bet a milestone payment on — calls with no procedure, real dollars, and your name on the outcome.
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 (12/20 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 (5/20) is whether the law requires a licensed human to sign. Trust premium (12/20) is whether buyers specifically pay for a person. Judgment and accountability (16/20) 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 33 of this occupation's 53 points (62%).
Embodiment (8/20) 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.
No occupation passed every test: close enough to natural sciences managers on skills and subject matter, at least 10 points more resistant, no big jump in training, no new licence, no pay cut, and not shrinking on its own. That happens for 223 of the 654 occupations here that aren't SAFE, and it is worth stating plainly rather than leaving the section off.
The usual reason is that exposure travels with the skill profile. The jobs most similar to yours tend to be exposed for the same reasons yours is, so the near neighbours don't clear the gap — and the ones that do are a different kind of work, not a transfer of what you already know. Read that as a limit of this method, not a verdict that you're stuck: it only compares whole occupations, and it cannot see specialisation, industry, or anything you'd bring that isn't in a federal skill survey.
Here is that claim on your own job rather than in the abstract. These are the three occupations closest to this one by skill and subject matter — the places the work would most naturally transfer — with what the register scores them:
That is the whole problem in three lines. The nearest work is not meaningfully safer, so there is no move here that trades a similar skill set for a better verdict. This is not us running out of ideas — it is what the neighbourhood looks like.
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 66/100, still EXPOSED.
Task-mix shift is genuinely available here: the occupation has a clear routine tier (grant narrative drafting, progress reports, literature summaries, budget spreadsheets, protocol completeness checks) and a judgment tier (portfolio kill/fund decisions, scientist hiring and development, safety walkthroughs, defending results to funders). If the routine tier is absorbed and headcount is not cut proportionally, the residual day is mostly the resistant tier.
Named-individual accountability written into statute or funder rule rather than org policy: e.g. an expansion of the NIH/NSF Responsible Conduct of Research and research-security regime (post-CHIPS Act Section 10632 research security program requirements) that requires a designated senior research official to personally certify data integrity, foreign-influence disclosures, and AI-use disclosure in submissions — with False Claims Act exposure for the signer. Precedent exists: institutional officials already sign grant certifications; individual liability is the missing piece.
For managers over biosafety, select agents, or radiation work, statutory naming of a Responsible Official under the CDC/APHIS Select Agent Program (42 CFR 73) or an NRC-licensed Radiation Safety Officer is already an individual, non-delegable, personally sanctionable role. If employers consolidate these designations into the natural sciences manager job description — or if a rule barred AI-generated safety determinations from satisfying the RO's inspection duty — the shield hardens for that subset.
Already near ceiling. Marginal upward movement only if AI-attributed research failures (retractions, failed trials traced to model-generated protocol errors) produce institutional rules naming a human manager as the accountable reviewer of any AI-influenced go/no-go decision — the pattern in FDA's draft guidance on AI in regulatory decision-making, which asks for a human accountable for model credibility.
The limit. Realistic ceiling is roughly the mid-60s. judgment_accountability is already 16 and the physical component is structurally small, so nearly all headroom sits in liability_shield — and that requires someone to convert existing institutional certification duties into named personal liability. Trust premium is omitted deliberately: the buyers here are funders, regulators, and executives who purchase institutional accountability, not a human manager per se, and there is no visible mechanism by which they would start paying a premium for human-ness.
| New York-Newark-Jersey City, NY-NJ | 10,100 | $180,460 +8% |
| Boston-Cambridge-Newton, MA-NH | 9,570 | $291,000 +74% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 5,890 | $169,060 +1% |
| San Francisco-Oakland-Fremont, CA | 5,530 | $225,230 +35% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 5,510 | $172,900 +3% |
| San Diego-Chula Vista-Carlsbad, CA | 2,890 | $207,200 +24% |
| Houston-Pasadena-The Woodlands, TX | 2,870 | $132,860 -21% |
| Los Angeles-Long Beach-Anaheim, CA | 2,530 | $193,340 +16% |
| Boston-Cambridge-Newton, MA-NH | 9,570 | $291,000 +74% |
| San Jose-Sunnyvale-Santa Clara, CA | 1,160 | $263,810 +58% |
| San Francisco-Oakland-Fremont, CA | 5,530 | $225,230 +35% |
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 53. 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.