What the score is trying to answer
The score answers one question: how healthy is the hiring market for this occupation right now, given what official sources have actually published. It is a market signal, not a prediction about any individual job search, and it is not a quality or desirability rating.
Three components combine into the composite. Each is scaled to a 0 to 100 range so that occupations of very different sizes remain comparable, then weighted as shown below.
Current Demand
BLS JOLTS and BLS CES
The near term hiring signal, and the largest single weight. It reads job openings, hires, and layoffs and discharges from JOLTS, together with employment and payroll momentum from CES.
Layoffs and discharges are inverted, so rising involuntary separations pull a score down rather than counting as activity. Because JOLTS and CES publish by industry rather than by occupation, the signal is weighted toward the industry mix that employs each occupation.
Occupation Fundamentals
Occupational employment trend and labor availability
The structural condition of the occupation itself: how its employment level has been trending, and how tight or available labor is for that kind of work.
This component is not a pay measure. Wage levels are shown on occupation pages as context and are not an input to any part of the score.
Long-Term Outlook
BLS Employment Projections
Projected employment growth over the official projection horizon, together with projected average annual openings, which combine growth with replacement of workers who leave the occupation.
It carries the smallest weight on purpose. Projections describe the published projection horizon, while a job search happens in months, so near term demand matters more to the composite.
What is context, not score
Occupation pages show more than the score. Median annual wages and metro employment come from BLS OEWS, and occupation descriptions, alternate titles and skills come from O*NET. Both are supporting context. Neither feeds the score.
Keeping pay out of the score is deliberate. A well paid occupation that is barely hiring is not a healthy market, and a moderately paid occupation adding people quickly is. Mixing the two would make the number harder to interpret rather than easier.
Normalization and comparability
Raw source values are not comparable across occupations on their own. An occupation employing two million people and one employing forty thousand produce openings and hires counts of entirely different magnitudes. Each component is therefore scaled against a comparable baseline before weighting, so that the composite reflects relative condition rather than absolute size.
Beyond that, this page does not describe internal transformation details that the published implementation does not itself expose. Where the score reports a component value, that value is shown directly on the occupation page rather than being summarized here.
Score history, revisions and restatements
Federal data gets revised. When a source revises an earlier month, the affected scores are recalculated. That is why each month can carry two values: the score as first published, and the restated score reflecting later revisions. Occupation pages show the published score alongside a restated value when one exists.
Each score also carries a confidence rating and the reasons behind it, so a month built on a partial or stale source is not presented as though it were fully supported.
Versioning and provenance
This is methodology version 1.0. When weights or inputs change, the change is published as a new version rather than quietly rewriting history, and scores record the methodology version they were computed under. Every occupation page names the release behind each figure and the date it was imported.
More on release cadence, coverage and limitations is on the about the data page, and each dataset has its own page under data sources.
Limits worth stating plainly
- Coverage is national. There is no local or state level score today.
- Published data is lagged. The most current month available is weeks behind the calendar, and occupational sources are annual.
- Monthly demand data is collected by industry, so occupation level demand is weighted exposure rather than a measured occupational vacancy count.
- Nothing is estimated to fill a gap. When a source has not published a value, the page says so.