EXPOSED
The modal data scientist spends most of the week on work AI already does credibly: writing pandas/SQL, cleaning and joining tables, building baseline models, tuning hyperparameters, generating charts, and writing up findings in decks and notebooks. What resists is upstream and downstream — turning a vague business question into a measurable target, deciding whether an observed lift is causal, catching leakage and biased sampling before a model ships, and standing behind a recommendation that moves pricing, credit, or headcount. No license protects the role, and it is fully screen-based, so the shrinkage lands on the analysis-execution tier while the framing-and-accountability tier holds.
Headcount grew steadily across the period.
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.
BLS projection, 2024–2034
+33.5% 245,900 → 328,300 on the projections basis
Exposed, but growing
AI can already do a lot of these tasks, and the BLS still expects +33.5% 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.
~23,400 openings a year on average, including replacing people who leave.
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Analytics Engineer
Holding it up: judgment & accountability . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier At 10, roughly half your week — feature engineering scripts, gradient-boosting baselines, cross-validation loops, matplotlib panels, notebook write-ups — is now competently drafted from a prompt, while defining the label for "churn" in a business with three contract types, spotting that your training set leaked post-outcome fields, or choosing a difference-in-differences design over a naive A/B split still needs you at the whiteboard; it sits below 14 because those framing hours are a minority of logged time, and above 6 because a model that ships without them fails in production.
Fully desk- and screen-based A 2 reflects that everything from data pull to stakeholder deck happens on a laptop against cloud compute, with the only physical residue being whiteboard sessions and in-person readouts that videoconference replaces without loss.
No licence, no signature requirement At 2 there is no board, no exam, no continuing-education requirement — a self-taught bootcamp graduate and a PhD hold the same title, and when a scoring model produces disparate impact under ECOA or the FCRA, the bank's compliance officers and counsel answer for it while your name appears nowhere on a filing.
Exists to be accountable for ambiguous calls 14 is earned by the calls with no correct procedure: whether a 0.7% AUC gain justifies a model that is unexplainable to a regulator, whether to tell leadership their favored experiment was underpowered from the start, whether known selection bias in the training population is tolerable for a pricing or credit-limit decision — you own these before anyone else can audit them, though it stops short of 17+ because a director or model-risk committee typically signs the deployment.
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 (2/20) 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 25 of this occupation's 37 points (68%).
Embodiment (2/20) 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 data scientists 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 58/100, still EXPOSED.
Model-risk governance rules extending SR 11-7-style validation duties beyond banks: EU AI Act high-risk obligations (Annex III: credit scoring, employment screening, insurance pricing) require a named human to document data governance, bias testing and sign the conformity assessment; Colorado SB 24-205 and NYC Local Law 144 bias-audit regimes push the same. If firms designate data scientists as the accountable signer of record for high-risk model documentation, this rises from 2 to 8-10.
Task-mix shift, no law needed: as codegen absorbs the pandas/SQL/baseline-model/charting tier, the remaining week concentrates on causal identification, experiment design under interference, leakage and sampling-bias detection, and metric definition — areas where AI output cannot be verified without the same expertise. If routine execution falls below ~30% of hours, task_resistance moves toward 13-14, though the count of workers needed to do the residual falls.
Formalized model-risk sign-off with personal attribution: an internal model inventory naming an owner per model, plus regulator-facing attestation (as bank MRM already does) making the data scientist the person who answers for a bad decision, not an anonymous team.
Insurer requirement: tech E&O / AI liability policies conditioning coverage on a documented human validation step by a named quantitative reviewer before model deployment — mirrors how cyber insurers made MFA a condition.
Narrow route only: expert-witness, regulatory-submission, and litigation/audit contexts (FDA statistical review, antitrust damages models, algorithmic discrimination cases) where a courtroom or agency requires a deposable human author of the analysis. Buyers pay for the attributable human, not the analysis.
The limit. Screen-based work keeps embodiment near 2 permanently, and no licensure body (no CPA/PE equivalent) exists or is being seriously proposed for data science, so liability_shield gains would come via employer/insurer designation rather than a true personal license — a weaker, more revocable shield. Even with all levers, headcount can shrink sharply while the residual role scores higher.
| New York-Newark-Jersey City, NY-NJ | 23,160 | $135,980 +13% |
| San Francisco-Oakland-Fremont, CA | 10,460 | $170,110 +41% |
| Dallas-Fort Worth-Arlington, TX | 10,120 | $127,750 +6% |
| Los Angeles-Long Beach-Anaheim, CA | 9,850 | $129,740 +8% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 9,260 | $132,200 +10% |
| Seattle-Tacoma-Bellevue, WA | 8,370 | $164,740 +37% |
| Chicago-Naperville-Elgin, IL-IN | 7,940 | $107,640 -10% |
| Boston-Cambridge-Newton, MA-NH | 7,930 | $132,040 +10% |
| San Jose-Sunnyvale-Santa Clara, CA | 6,060 | $185,080 +54% |
| San Francisco-Oakland-Fremont, CA | 10,460 | $170,110 +41% |
| Idaho Falls, ID | 230 | $167,840 +40% |
The Seattle Times reported that Amazon cut jobs within its artificial intelligence team.
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.