The market, read off the records.
Not a forecast and not a think piece. Three charts computed from the same career records you can open yourself, which is what makes every average on them checkable rather than quotable.
Averages you can take apart.
An average is where information goes to hide. Every figure here is drawn from career records held on the same scale, so the number is followed by the spread behind it and by the individual roles that produced it. A field average of 46 made of two clusters at 20 and 70 is a different fact from one made of roles all sitting at 46, and only one of those is a safe bet.
- Technology125 rolesKES 93,11667 / 62+5
- Engineering113 rolesKES 78,20658 / 42+16
- Health Sciences96 rolesKES 73,07771 / 28+43
Averages hide the range. A field averaging 46 holds roles at 10 and roles at 90, which is what the distribution chart is for.
What it actually does.
Hiring against exposure
A scatter with a break-even diagonal, circle area set by the number of roles in the field. Below the line, hiring outpaces what a machine can do. Above it, the field is adding exposure faster than it is adding jobs.
The spread, not just the mean
A histogram across ten bands, because the distribution is bimodal: an average sitting in the middle of it describes almost nobody, and the two clusters it hides are the actual finding.
Entry pay by field
Its own chart rather than a third axis on the first two, because pay is in shillings and the other scores are 0 to 100 index numbers. Putting them on one axis would invent a relationship.
Ranked by hiring minus exposure
Careers ordered by the gap rather than by hiring alone, which is the ordering that answers what to actually do. A well-hiring role with high exposure is not the same opportunity as a quieter, durable one.
Your fields first
The fields you named are one tab and everything else is the next, so the read starts where your decision is rather than at whatever is top of the index.
Where every number comes from.
There is no separate trends dataset. The charts are computed from the catalogue at read time, which is the only reason a figure here can never disagree with the record behind it.
- 01
Records carry the scores
Each career holds its hiring outlook, exposure rating and pay band on the same scale as every other.
- 02
Averaged by field
Grouped by category, with the count kept beside the mean so a thin sample is visible.
- 03
Plotted on one scale
Hiring and exposure share an axis because they share a range. Pay gets its own chart.
- 04
Opened and checked
Every point is a group of records you can read individually, which is where an average stops being a claim.
Who gets it.
Every tool is on every tier. What a tier buys is credits, which is how much model work you can spend in a week.
The catalogue is open today and needs no account. The app itself is in invite-only beta.
Compare tiers- FreeKES 0
- IncludedIncluded
- ProKES 500 a month
- IncludedIncluded
- ProfessionalKES 900 a month
- IncludedIncluded
What it does not claim.
These are charts about a catalogue, and the catalogue is a description of roles rather than a measurement of the economy.
5 stated
- 01It is not a labour market survey. Nothing here counts vacancies, hires or wages as they happen; it summarises what the records say about each role, which is a different thing.
- 02Hiring outlook is an assessment, not a headcount. It is maintained per record against published sources, and it inherits whatever is stale in them.
- 03Thin fields produce loud averages. A category with nine careers moves on one edit, and the count is shown beside every mean so you can discount it yourself.
- 04Exposure ratings are estimates. Each is built from a task split published on the record, and the whole point of showing the spread is that the middle of a bimodal distribution describes nobody.
- 05It says nothing about your county. Everything is national, and the difference between Nairobi and elsewhere is real and not modelled here.
Common questions.
If yours is not here, ask. We answer in writing so the answer is quotable, and we correct the record when we get one wrong.
Ask usCheck the average against the rows.
Every point on every chart is a set of career records. Start with the field you are weighing and read what it is actually made of.