Monthly Market Report
50
postings scored across 41 companies
Fit scores, skill demand, salary transparency, ghost job analysis.
50
Jobs Analyzed
50 scored
41
Companies Hiring
unique employers
58
Avg Fit Score
out of 100
66%
Salary Disclosed
33 listings
30%
Remote Listings
of all postings
0%
Ghost Signal Rate
of postings
The scoring engine processed 50 job postings in February 2026, running them against active candidate profiles to generate 50 fit scores. The average fit score was 58 — lower than you'd want, and a sign that this month's postings had specific requirements that most profiles didn't fully meet. 21 postings (42%) scored below 60, meaning the fit was too thin to compete without significant profile improvement.
On the demand side, E-commerce, Bachelor's Degree, Data Analysis led across all scored postings. The gap picture this month was relatively distributed, without a single skill dominating as a universal weakness. The L-level breakdown — which measures required skill depth from L1 (basic awareness) to L5 (architect-grade expertise) — reveals which skills are commoditized versus genuinely differentiating. Skills sitting at L3 or higher are where candidates get separated from the pack.
Salary transparency was above average at 66% — enough to draw meaningful conclusions about compensation ranges. 30% of postings were listed as remote or remote-friendly. Ghost job signals were relatively low at 0%, suggesting above-average listing quality this month. The salary data, skill rankings, and company breakdown below pull from the same scored dataset — not survey data, not self-reported figures.
How scored postings spread across the 0–100 range. Scores below 60 represent thin fit; 75+ is where applications compete well.
21
Below 60 (weak fit)
29
60–74 (partial fit)
0
75+ (strong fit)
Fit scores weight profile match at 70% and resume match at 30%. A score of 75+ means the candidate's skills, experience level, seniority, and logistics overlap enough to compete. See how fit scores work for the full methodology.
Ranked by how often each skill appeared in scored postings. L-level indicates the typical required depth: L1 is basic familiarity, L5 is architecture-level expertise. Skills above L3 signal roles where depth actually matters. Full skill profiles at /skills.
Depth levels (L1–L5) derived from how surrounding job description context describes required experience. See the L-level system explained. Browse all tracked skills at /skills.
Based on 33 postings that disclosed compensation out of 50 total (66% transparency rate). Midpoint is used where both min and max are listed.
$115K
Median Salary
$122K
Average Salary
$70K
Floor
$215K
Ceiling
$117K
Average — Remote listings
$123K
Average — On-site listings
+$6K above remote avg
Roles with at least 3 salary-disclosing postings
| Role | Median | n |
|---|---|---|
| Software Engineering | $130K | 3 |
| Other | $112K | 8 |
| Marketing | $116K | 20 |
Companies with the most active postings this month. Avg score reflects how well those postings matched the candidate profiles that viewed them. High ghost rates suggest the company posts frequently but may not actively fill those roles. Company profiles at /companies.
| # | Company | Listings |
|---|---|---|
| 1 | Amazon Web Services, Inc. | 5 |
| 2 | Agoda | 4 |
| 3 | Seattle Bank | 2 |
| 4 | EDF | 2 |
Ghost jobs are postings that show low hiring intent — old posting dates, no salary disclosure, and generic descriptions that suggest the role isn't actively filling. ShouldApply scores each listing across multiple quality signals. Learn how ghost job detection works.
0%
of postings with ghost signals
0
postings with at least one ghost signal
50
postings with clean quality signals
Ghost signals are based on: posting age (45+ days), absence of salary data, and vague job description content. A listing can have one or more signals. The dashboard flags these automatically so you can deprioritize them. Full ghost job methodology.
Patterns worth noting from this month's dataset. Not statistical projections — just what the numbers show.
E-commerce, Data Analysis, Digital Marketing averaged 4.9+ on the depth scale this month — meaning postings weren't looking for familiarity, they required working fluency. Skills at L5+ are where candidates get separated from the pile. E-commerce skill profile →
By contrast, Product Marketing appeared frequently but at low depth (L2), which means they're table stakes — worth having, but not differentiating.
Remote listings averaged $117K vs. $123K for on-site — a $6K gap. With 30% of postings flagged as remote, the supply of remote work is still tighter than demand.
66% of postings included compensation data this month. The median was $115K, which holds in line with market expectations for the skills in demand. State-level salary transparency laws (Colorado, New York, Washington) push overall rates up, but the remaining 34% of listings still leave candidates negotiating blind.
Data transparency matters. Here's exactly what goes into these numbers.
Job postings are pulled from five sources: JSearch, Remotive, Adzuna, Arbeitnow, and Wellfound. Each source is refreshed every 2–6 hours. Cross-source duplicates are removed using a SHA-256 content hash plus Jaccard title similarity (threshold: 0.8) within the same company.
Quality filters remove thin descriptions (under 100 words), postings from blocked domains, and non-English listings. What's left goes into the scoring pipeline.
Each posting is scored against a candidate's profile using a five-dimension model: Skills Match, Experience Level, Seniority Alignment, Industry Fit, and Logistics (salary, remote, location). The overall score is 70% profile fit + 30% resume match.
Skill depth (L1–L5) is extracted from surrounding context in the job description — not just keyword presence. SHA-256 input hashing prevents re-scoring identical profile+JD combinations, keeping the dataset efficient.
A posting is flagged as having ghost signals if it was posted more than 45 days ago and includes no salary data. This is one component of a broader additive ghost probability model (capped at 95%) that also weighs applicant count, vague description quality, and reposting patterns. See ghost job methodology for the full model.
Monthly reports are computed from all jobs created during the calendar month. This page is cached with a 7-day ISR window — data updates weekly as new postings are scored. Salary figures use the midpoint of disclosed min/max ranges where both values are present. Minimums of 3 data points are required before salary stats are shown.
See how you stack up
Upload your resume, get scored against 50 postings from February, and see exactly where you match and where you don't.