ATSRadar Blog — #ats (1 post)

ATSRadar analyzed its own historical job and polling data to show how quickly openings appear, change, and disappear, and why applying early matters.

Most job seekers know that "apply early" is good advice. The harder question is: how early actually matters?

Some openings stay active for weeks. Others look fresh one day and are gone the next time you check.

This report uses ATSRadar's own historical jobs and polling data to answer four practical questions:

How long do openings usually remain active after ATSRadar first sees them?
How much of the active market is truly fresh right now?
Do some ATS ecosystems or role families move faster than others?
What should job seekers change if they want to apply earlier?

Key takeaways

Observed median time-to-inactive: 8.95 days for jobs that have already closed after ATSRadar first saw them.
Active jobs are not mostly brand new: the median currently open role has already been in ATSRadar for 24.54 days.
Early closure is real: 14.02% of mature cohorts go inactive within 7 days, and 31.53% are inactive within 14 days, rising to 49.03% within 30 days.
The truly fresh slice is small: only 1.59% of currently active jobs were first seen in the last 24 hours, and 21.30% were first seen in the last 7 days.
What this means for job seekers: the best openings do not vanish instantly across the board, but the early-application edge is strongest in the first 7-14 days.

What "fast-moving" means in this report

This article measures observed opening speed, not the exact amount of time a job has existed on the employer side.

That distinction matters.

First seen means when ATSRadar first ingested the opening.
Inactive means when ATSRadar detected that the opening had left the active set.
postedAt is shown for coverage context only because many ATS records do not provide it consistently enough to anchor lifecycle math.

So when this article says a role "closed within 14 days," it means ATSRadar first saw it, then later detected it inactive within 14 days.

That is the right frame for job seekers deciding how often to check alerts, how quickly to apply, and how much freshness should influence prioritization.

Data breakdown

What the data is measuring

This article measures observed opening speed, not employer-side posted duration. In practice that means we anchor lifecycle math to the moment ATSRadar first sees a job (created_at) and the moment ATSRadar detects it has left the active pool (became_inactive_at).

Current analysis window: 90 days ending 2026-03-31 UTC. Jobs analyzed in-window: 441,014.

Chart 1: Observed time-to-inactive buckets

This chart uses only jobs that have already gone inactive, so it describes observed closed-role lifetimes after ATSRadar first saw the posting.

Share of inactive jobs

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Bucket	Inactive jobs	Share of inactive jobs
<3 days	29,189	16.83%
3-7 days	26,554	15.31%
7-14 days	64,411	37.13%
14-30 days	46,162	26.61%
30+ days	7,156	4.13%

Bucket	Inactive jobs	Share of inactive jobs
<3 days	29,189	16.83%
3-7 days	26,554	15.31%
7-14 days	64,411	37.13%
14-30 days	46,162	26.61%
30+ days	7,156	4.13%

Chart 2: How fresh the current active pool actually is

Share of current active jobs

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First seen bucket	Active jobs	Share of current active jobs
24h	4,263	1.59%
3d	15,411	5.76%
7d	56,978	21.30%
14d	88,103	32.93%

First seen bucket	Active jobs	Share of current active jobs
24h	4,263	1.59%
3d	15,411	5.76%
7d	56,978	21.30%
14d	88,103	32.93%

Chart 3: Daily flow of openings into and out of the active pool

New jobs first seenJobs became inactiveNet active pool change

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Day	New jobs	Jobs became inactive	Net active pool change
2025-12-31	0	0	0
2026-01-01	0	0	0
2026-01-02	0	0	0
2026-01-03	0	0	0
2026-01-04	0	0	0
2026-01-05	0	0	0
2026-01-06	0	0	0
2026-01-07	0	0	0
2026-01-08	0	0	0
2026-01-09	0	0	0
2026-01-10	0	0	0
2026-01-11	0	0	0
2026-01-12	0	0	0
2026-01-13	0	0	0
2026-01-14	0	0	0
2026-01-15	0	0	0
2026-01-16	0	0	0
2026-01-17	0	0	0
2026-01-18	0	0	0
2026-01-19	0	0	0
2026-01-20	0	0	0
2026-01-21	0	0	0
2026-01-22	0	0	0
2026-01-23	0	0	0
2026-01-24	0	0	0
2026-01-25	0	0	0
2026-01-26	0	0	0
2026-01-27	0	0	0
2026-01-28	0	0	0
2026-01-29	0	0	0
2026-01-30	0	0	0
2026-01-31	0	0	0
2026-02-01	0	0	0
2026-02-02	0	0	0
2026-02-03	0	0	0
2026-02-04	0	0	0
2026-02-05	0	0	0
2026-02-06	0	0	0
2026-02-07	0	0	0
2026-02-08	0	0	0
2026-02-09	0	0	0
2026-02-10	0	0	0
2026-02-11	0	0	0
2026-02-12	0	0	0
2026-02-13	0	0	0
2026-02-14	0	0	0
2026-02-15	0	0	0
2026-02-16	0	0	0
2026-02-17	0	0	0
2026-02-18	56,308	366	+55,942
2026-02-19	64,168	777	+63,391
2026-02-20	5,815	3,895	+1,920
2026-02-21	1,091	969	+122
2026-02-22	77	153	-76
2026-02-23	6,522	1,810	+4,712
2026-02-24	5,063	3,737	+1,326
2026-02-25	2,454	2,760	-306
2026-02-26	3,317	3,140	+177
2026-02-27	33,356	29,172	+4,184
2026-02-28	6,167	5,359	+808
2026-03-01	17,612	237	+17,375
2026-03-02	3,487	1,723	+1,764
2026-03-03	3,093	4,449	-1,356
2026-03-04	5,065	4,842	+223
2026-03-05	3,700	3,874	-174
2026-03-06	89,154	3,404	+85,750
2026-03-07	2,880	2,721	+159
2026-03-08	4,891	444	+4,447
2026-03-09	3,525	5,154	-1,629
2026-03-10	3,418	7,794	-4,376
2026-03-11	1,047	973	+74
2026-03-12	2,429	2,150	+279
2026-03-13	3,203	3,081	+122
2026-03-14	703	701	+2
2026-03-15	10,730	10,262	+468
2026-03-16	670	850	-180
2026-03-17	322	265	+57
2026-03-18	3,386	3,394	-8
2026-03-19	17,565	18,878	-1,313
2026-03-20	8,400	6,166	+2,234
2026-03-21	2,890	3,695	-805
2026-03-22	0	2	-2
2026-03-23	1,252	6,952	-5,700
2026-03-24	8,879	5,577	+3,302
2026-03-25	4,126	7	+4,119
2026-03-26	34,904	10,188	+24,716
2026-03-27	748	1,414	-666
2026-03-28	14,126	6,584	+7,542
2026-03-29	210	280	-70
2026-03-30	2,844	5,275	-2,431
2026-03-31	1,420	0	+1,420

Day	New jobs	Jobs became inactive	Net active pool change
2025-12-31	0	0	0
2026-01-01	0	0	0
2026-01-02	0	0	0
2026-01-03	0	0	0
2026-01-04	0	0	0
2026-01-05	0	0	0
2026-01-06	0	0	0

Showing first 7 of 91 rows.

Chart 4: Which ATS ecosystems move faster

Only providers above the minimum volume threshold (5,000 jobs and mature 14-day cohorts) are included.

Share inactive within 14 days

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ATS	Jobs in window	Median observed time-to-inactive	Inactive within 7d	Inactive within 14d	Inactive within 30d
Greenhouse	214,222	13.15 days	6.52%	20.41%	41.18%
Ashby	88,512	8.94 days	15.51%	52.74%	64.10%
Lever	77,072	6.51 days	25.87%	33.33%	41.76%
SmartRecruiters	35,125	5.39 days	27.32%	33.29%	37.50%

ATS	Jobs in window	Median observed time-to-inactive	Inactive within 7d	Inactive within 14d	Inactive within 30d
Greenhouse	214,222	13.15 days	6.52%	20.41%	41.18%
Ashby	88,512	8.94 days	15.51%	52.74%	64.10%
Lever	77,072	6.51 days	25.87%	33.33%	41.76%
SmartRecruiters	35,125	5.39 days	27.32%	33.29%	37.50%

Chart 5: Which role families close faster

Family inference coverage: 66.54% of jobs landed in a non-Other family.

Median observed time-to-inactive (days)

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Role family	Jobs in window	Median observed time-to-inactive	Inactive within 7d	Inactive within 14d	Inactive within 30d
Engineering	85,864	8.94 days	13.61%	33.60%	49.22%
Design	71,990	8.98 days	20.51%	35.43%	48.03%
Security	33,783	12.50 days	10.38%	29.08%	47.91%
Sales	29,214	8.95 days	7.90%	30.72%	50.59%
Operations/RevOps	16,438	8.94 days	11.55%	37.05%	55.75%
Marketing	12,565	8.95 days	11.29%	37.14%	55.78%
Data	9,757	8.95 days	14.53%	33.35%	52.28%
HR/People	9,293	13.10 days	12.61%	40.90%	62.62%
Finance	8,571	9.38 days	9.68%	32.38%	56.34%
Product	6,561	8.98 days	8.92%	31.38%	50.20%

Role family	Jobs in window	Median observed time-to-inactive	Inactive within 7d	Inactive within 14d	Inactive within 30d
Engineering	85,864	8.94 days	13.61%	33.60%	49.22%
Design	71,990	8.98 days	20.51%	35.43%	48.03%
Security	33,783	12.50 days	10.38%	29.08%	47.91%
Sales	29,214	8.95 days	7.90%	30.72%	50.59%
Operations/RevOps	16,438	8.94 days	11.55%	37.05%	55.75%
Marketing	12,565	8.95 days	11.29%	37.14%	55.78%

Showing first 6 of 10 rows.

Geography note

ATSRadar stores a raw country value for only 36.16% of jobs in this historical first-seen window, so this article does not publish country speed rankings. That is deliberate: weak normalization would create false precision.

What job seekers should do with this

If a role matters, treat the first 7-14 days as the highest-value application window instead of assuming it will sit open for a month.
Keep one broad alert for volume and one tighter alert for the exact titles you want, so you can react quickly without drowning in noise.
When a provider or role family in this report moves faster, check those openings daily rather than weekly.
Use freshness as a filter, not a guarantee: a job first seen yesterday is not automatically better, but it is less likely to be late-stage in the hiring process.

Why applying early still matters

The data does not say that every job disappears instantly.

It does say that the first week and the first two weeks matter more than many job seekers assume.

If you only check once a week, you will still catch part of the market. But you will be consistently late on the slice that moves fastest, and that is often the exact slice with the least competition slack.

In practical terms:

Use alerts or searches that surface newly seen roles daily.
Prioritize the first 7-14 days, not just the first 24 hours.
Use freshness to rank your queue, especially in faster ATS ecosystems and faster-moving role families.
Do not rely on postedAt alone when it is missing or stale. First-seen timing is often the more reliable operational signal.

Methodology

Analysis window: 90 days ending 2026-03-31T04:08:06.489Z (UTC).

Dataset coverage: first observed job 2026-02-18T06:53:08.863Z, latest observed lifecycle timestamp 2026-03-31T04:08:06.489Z.

Field choices: first seen = jobs.created_at is used as first seen by ATSRadar because it marks first ingestion into the platform. last seen = jobs.last_seen_at is used as the most recent confirmed observation of an opening. became inactive = jobs.became_inactive_at is used as the detected closure timestamp when a previously active opening drops out of the active set.

postedAt coverage: 219,224 jobs in-window (49.71%).

Role-family coverage: 66.54% classified outside Other.

Exact vs Approximate

Exact: first seen = jobs.created_at (job first ingested by ATSRadar)
Exact: last seen = jobs.last_seen_at (most recent successful observation)
Exact: became inactive = jobs.became_inactive_at (when ATSRadar detected the job left the active set)
Exact: freshness buckets use current active jobs only and first-seen timestamps
Exact: daily flow uses UTC day buckets from created_at and became_inactive_at
Approximate: time-to-inactive is exact to ATSRadar detection time, not the exact moment an employer removed the posting
Approximate: median observed lifetime only covers jobs that have already become inactive inside the analysis window
Approximate: postedAt reflects source-provided employer timestamps and is used for coverage notes only because completeness is mixed
Approximate: older inactive rows that predate became_inactive_at were backfilled from updated_at / last_seen_at / scanned_at fallbacks during migration 0033
Approximate: geography is not published in this article because stored country coverage is not strong enough for a reliable all-cohort speed comparison

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ATSRadar Blog — #ats (1 post)

How Fast Job Openings Actually Move

Key takeaways

What "fast-moving" means in this report

Data breakdown

What the data is measuring

Chart 1: Observed time-to-inactive buckets

Chart 2: How fresh the current active pool actually is

Chart 3: Daily flow of openings into and out of the active pool

Chart 4: Which ATS ecosystems move faster

Chart 5: Which role families close faster

Geography note

What job seekers should do with this

Why applying early still matters

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