How Reqbeat measures hiring demand — the sample, the deduplication rule, the known biases, and the metric definitions we have frozen in advance.
We read public job postings from company applicant-tracking systems, public job boards and aggregators, and admit each one to the corpus as it is crawled.
What we measure is the volume of online job postings we observe, deduplicated to one row per company, job title and country.
Repeat and re-posted advertisements collapse to a single row per (company, job title, country). Where several postings share that key, we keep the most complete one — the row that resolves to a named company with an industry, headcount and location — and break remaining ties by the most recent posting date. Note the key is country, not city: two postings for the same role in two cities of one country count once, and the same role in two countries counts twice.
The corpus surface our published research reads retains a rolling 30-day window on posting date. Any metric requiring a longer observation span than that is listed below as pre-registered and is not published until the retention exists to support it.
These definitions are frozen and dated before any figure is computed under them, so a threshold can never be tuned after seeing a result. A metric marked pre-registered has no published figure — the reason is stated with it.
A posting that remains open, or is re-listed under the same deduplication key, for at least 90 days without disappearing from its source.
Observation window required: 90 days
The median number of days a requisition stays live, from the first time we observe it to the point it disappears from its source.
Observation window required: unbounded
For postings we observe on two or more sources, the median gap in hours between the first time the earliest source shows it to us and the first time the latest source does.
Observation window required: 30 days
A monthly index of advertised hiring demand. Each edition's observation is the count of deduplicated postings — one row per company, job title and country, staffing agencies excluded — posted in the 28 days ending on that edition's publication slot. The index expresses that observation as a percentage of the base edition's observation, which is fixed at 100.0. The base edition is the first edition published under this definition.
Observation window required: 28 days
For each reference month in which both series have a print, the Hiring-Demand Index's month-over-month percentage change minus the official series' month-over-month percentage change for that month, in percentage points, together with the share of those months in which the two moved in the same direction. An edition's reference month is the calendar month holding the majority of the 28 days its observation was counted over. The comparison is made against the official series' first published print for the month, never a later revision. Movement is compared, never level: this index counts advertised vacancies in a sample of public postings and does not estimate the vacancy stock an official survey measures.
Observation window required: unbounded
The whole-month shift — searched over 0 to 3 months — at which the Hiring-Demand Index's month-over-month direction agrees most often with the official series', reported together with the agreement at every shift tested so a shift that wins by one month is visible as winning by one month.
Observation window required: unbounded
Some studies count postings by matching their titles against a fixed list of terms. Those lists are published here in full and dated, so the window they define cannot be widened or narrowed after a result is seen without that change being published as a revision.
Terms are matched against a posting's job title, case-insensitively and on a word boundary — so “llm” matches “LLM Engineer” and not “fulfillment”. They are not matched against the description of the posting: the corpus surface our research reads carries a posting's title, company, location and salary but not its body text, so a posting whose body mentions a term under an unrelated title is not counted. Every list below therefore understates its term's true incidence, in the same direction and for the same reason.
Titles that name the model-building work itself. Deliberately excludes “ai” alone, which matches unrelated titles on a word boundary, and the vendor and framework names that trend and fade faster than a monthly study's cadence can honestly track.
Titles for building and moving the data the models are trained and served on — the pipeline half of the same team, kept separate so growth in one is not read as growth in the other.
Titles for analysis and experimentation rather than production systems. Narrow on purpose: broadening it toward “analyst” would absorb business reporting roles and inflate the cut.
Titles for the runtime the above is deployed onto. Included as the comparison set: it is the specialism whose demand would have to fall if model work were displacing infrastructure work rather than adding to it.
Titles for securing the systems above. A second comparison set, and the one least coupled to model adoption — a control against reading a market-wide hiring swing as a specialism-specific one.
Some studies rank demand across sectors. The sectors are published here in full and dated, so a taxonomy cannot be redrawn around a result after the result is seen without that change being published as a revision.
Industry labelling does not cover every posting, and its coverage is uneven across sources. So any figure we publish by industry is a rate measured inside a sector — the same labelled population in the numerator and the denominator — never a share of the whole corpus, and it excludes every posting whose employer carries no industry label.
Industry groups are matched against the industry label the source itself publishes for the employer, case-insensitively and as a substring — so “software” matches “Software Development”. Substring matching is looser than the word-boundary rule used for job titles, which is why every term below is a phrase specific enough that no unrelated label contains it. A label may match more than one group; each group is measured against its own postings, so an employer counted in two groups is counted correctly in both rather than twice in one.
The sector that builds software as its product. Expected to lead any engineering-demand ranking, and included precisely so that expectation is measured against the others rather than assumed.
Regulated, data-heavy, and an early adopter of model-driven work outside the technology sector — the clearest test of whether AI hiring has spread beyond employers whose product is software.
Research-intensive and slow-moving on hiring, so a rate that rises here is harder to explain as a labelling artefact than one that rises in software.
The largest employer group in several of the national corpora we read, and the one whose postings least often reach an English-language ATS — a control against reading source coverage as sector demand.
High posting volume, low engineering density. Included as the low end of the expected range: a ranking with no low end is a ranking whose scale a reader cannot judge.
Recommendation and content-generation work sits here, so it is where applied model work would appear first outside the technology sector proper.
Public-sector and academic postings follow a hiring calendar rather than a market, so this group is expected to move differently from the rest and is kept separate rather than folded into professional services.
Firms that sell expertise rather than a product. Their postings are often for client projects, which is stated here because it is the group whose demand is least attributable to the employer's own operations.
See also Data ethics — what we hold, and what we will not publish.