EXPOSED
Most of the week is surveillance data cleaning, R/SAS regression, literature review, and writing reports and grant sections — all things current models draft at usable quality with supervision. What holds is designing studies whose confounders aren't obvious, running field outbreak investigations with case interviews and site visits, and standing behind a case definition or exposure conclusion that drives a public health order. The modal worker here is a state/local health department or academic analyst, not a field investigator, so the automatable share of the job is larger than the profession's self-image suggests.
Headcount grew steadily across the period.
Median pay $70,990 → $87,220 -1.7% 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
+16.2% 12,300 → 14,300 on the projections basis
Exposed, but growing
AI can already do a lot of these tasks, and the BLS still expects +16.2% 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.
~800 openings a year on average, including replacing people who leave.
MalariologistEpidemiologistHistopathologistClinical ResearcherEpidemiology AnalystNurse EpidemiologistState EpidemiologistMedical EpidemiologistPharmacoepidemiologistClinical EpidemiologistResearch EpidemiologistEpidemiologist ResearcherEpidemiology InvestigatorInfection Control ManagerEnvironmental EpidemiologistEpidemiology Research DoctorInfection Control SpecialistInfection Preventionist (IP)Public Health EpidemiologistInfection Control CoordinatorInfection Control Nurse (ICN)Infectious Disease SpecialistChronic Disease EpidemiologistInfection Control Preventionist
Holding it up: judgment & accountability . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier A 10 reflects the split week: line-list cleaning, Poisson and Cox models in R, and drafting the background and methods sections of a grant are already model-drafted, while specifying a case definition mid-outbreak, deciding whether a cluster's excess is surveillance artifact or real transmission, and designing a cohort where the confounder is something nobody has measured yet still take a trained human — and that second set is a real third of the job, not a footnote.
Some physical or field component 7 covers the fraction of epidemiologists who do periodic field work — door-to-door or telephone case interviews, restaurant or long-term-care facility walkthroughs, specimen and environmental sampling coordination, PPE-donned site entry during an outbreak — against a baseline of most weeks spent entirely in NEDSS/SAS at a desk.
Certification preferred, not legally required At 5 you sit where the CIC or CPH credential is a hiring preference rather than a statute: the health officer or state epidemiologist signs the quarantine or closure order and carries the legal exposure, and nothing in your analysis requires a personally licensed signature the way a physician's or engineer's does.
Exists to be accountable for ambiguous calls 14 rests on calls that have no clean procedure and immediate consequences: setting the confirmed-versus-probable boundary when the assay is imperfect, deciding an exposure is causal enough to name a source publicly, recommending school closure or a contact-tracing scope on incomplete data, and defending that inference to press and legislators when a revision would look like error.
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 (5/20) is whether the law requires a licensed human to sign. Trust premium (9/20) is whether buyers specifically pay for a person. Judgment and accountability (14/20) 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 28 of this occupation's 45 points (62%).
Embodiment (7/20) 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.
Genetic Counselors EXPOSED
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 59/100, still EXPOSED.
If routine surveillance ETL, standard regression scripts, and literature screening are absorbed by tooling, the residual job becomes confounder-aware design, outbreak hypothesis generation, and defending a case definition — a genuine two-tier occupation where the judgment tier is what remains. Watch for state health departments consolidating analyst FTEs while retaining 'epidemiologist III/IV' design and investigation roles.
If CDC/CSTE or state statute requires a named, credentialed epidemiologist (e.g., CIC or state-registered epidemiologist) to attest to the analytic basis of a communicable disease control order or a reportable-condition case classification — analogous to how a laboratory director must sign out CLIA results — signature authority attaches to a person rather than an agency. Watch CSTE position statements on case definition attestation and post-COVID state public health authority reform bills.
If litigation over pandemic-era orders continues to name individual health officers and epidemiologists as deponents or defendants, and courts credit or discredit the analyst's reasoning under Daubert-style scrutiny in exposure and product cases, the role's ownership of ambiguous calls becomes formally attributable. Watch expert-witness admissibility rulings on AI-assisted epidemiologic analysis and state indemnification statutes for public health staff.
If field investigation capacity is deliberately rebuilt — funded epidemic intelligence service–style deployments, mandatory on-site environmental assessment for cluster investigations, in-person case interviewing for high-consequence pathogens — the share of the job requiring presence in uncontrolled settings rises. Watch state appropriations for field epidemiology positions and CDC outbreak response staffing rules.
The limit. Trust premium has no realistic route: the buyer is a government agency or grant funder that purchases an institutional conclusion, not a named human's judgment, and no patient or client chooses their epidemiologist. Even a strong liability shield leaves this an occupation where one signer can attest to work produced at scale, which caps headcount protection regardless of dimension scores.
| Boston-Cambridge-Newton, MA-NH | 620 | $115,890 +33% |
| Seattle-Tacoma-Bellevue, WA | 580 | $123,970 +42% |
| New York-Newark-Jersey City, NY-NJ | 400 | $98,340 +13% |
| Los Angeles-Long Beach-Anaheim, CA | 380 | $105,400 +21% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 380 | $112,780 +29% |
| Atlanta-Sandy Springs-Roswell, GA | 370 | $70,880 -19% |
| Denver-Aurora-Centennial, CO | 370 | $72,410 -17% |
| Olympia-Lacey-Tumwater, WA | 330 | $108,240 +24% |
| San Francisco-Oakland-Fremont, CA | 320 | $130,400 +50% |
| San Diego-Chula Vista-Carlsbad, CA | 160 | $125,320 +44% |
| Seattle-Tacoma-Bellevue, WA | 580 | $123,970 +42% |
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 45. 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.