Monthly Market Report
September 2026 Job Market
34
postings scored across 30 companies
Fit scores, skill demand, salary transparency, ghost job analysis.
Key Metrics
34
Jobs Analyzed
▼ 377 vs August
30
Companies Hiring
unique employers
48
Avg Fit Score
▼ 4pts vs August
35%
Salary Disclosed
▲ 3% vs August
41%
Remote Listings
of all postings
32%
Ghost Signal Rate
of postings
Executive Summary
The scoring engine processed 34 job postings in September 2026, running them against active candidate profiles to generate 34 fit scores. That's down from 411 the prior month. The average fit score was 48 — lower than you'd want, and a sign that this month's postings had specific requirements that most profiles didn't fully meet. The average moved down 4 points from last month's 52. 1 postings (3%) crossed the 75-point threshold — the range where a application is worth serious effort. 31 postings (91%) scored below 60, meaning the fit was too thin to compete without significant profile improvement.
On the demand side, Python, Bachelor's Degree, AWS led across all scored postings. Python led the gap list — appearing in dozens of postings where candidates consistently fell short of the required depth. These aren't obscure skills. A 20% gap rate across 10 postings is a systemic problem, not an outlier. 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 was disclosed in 35% of listings, which is workable but limits precision on the compensation side. 41% of postings were listed as remote or remote-friendly. Ghost job signals appeared in 32% of listings. ShouldApply flags these automatically in the dashboard so candidates can deprioritize them. The salary data, skill rankings, and company breakdown below pull from the same scored dataset — not survey data, not self-reported figures.
Fit Score Distribution
How scored postings spread across the 0–100 range. Scores below 60 represent thin fit; 75+ is where applications compete well.
31
Below 60 (weak fit)
2
60–74 (partial fit)
1
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.
Top In-Demand Skills — September 2026
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.
| # | Skill | Postings |
|---|---|---|
| 1 | Python | 10 |
| 2 | Bachelor's Degree | 4 |
| 3 | AWS | 4 |
| 4 | SQL | 3 |
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.
Biggest Skill Gaps
Skills where candidates most frequently fell below the required proficiency level. A high gap rate means this skill appears often in postings — and most candidates who applied were underprepared. These are the skills most worth closing before your next job search cycle.
| Skill | Gap Rate | Postings |
|---|---|---|
| Python | 20% gap | 10 |
How to use this list: Skills with a gap rate above 50% and an L3+ requirement are the highest-leverage areas to improve. They appear frequently, they matter to employers, and most candidates applying don't have the depth required. Closing one of these gaps can move your fit score significantly across dozens of relevant postings. Run your scores to see which of these affect you specifically.
Salary Insights
Based on 12 postings that disclosed compensation out of 34 total (35% transparency rate). Midpoint is used where both min and max are listed.
$128K
Median Salary
$142K
Average Salary
$81K
Floor
$225K
Ceiling
Remote vs. On-Site Compensation
$150K
Average — Remote listings
+$19K above on-site avg
$131K
Average — On-site listings
Salary by Role Category
Roles with at least 3 salary-disclosing postings
| Role | Median | n |
|---|---|---|
| Software Engineering | $128K | 8 |
Top Hiring Companies — September 2026
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 | Capital One | 2 |
Ghost Job Analysis
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.
32%
of postings with ghost signals
11
postings with at least one ghost signal
23
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.
Market Observations — September 2026
Patterns worth noting from this month's dataset. Not statistical projections — just what the numbers show.
Depth requirements are rising in specific areas
Python averaged 3.9+ on the depth scale this month — meaning postings weren't looking for familiarity, they required working fluency. Skills at L4+ are where candidates get separated from the pile. Python skill profile →
By contrast, SQL appeared frequently but at low depth (L1), which means they're table stakes — worth having, but not differentiating.
Remote roles paid more this month
Remote listings averaged $150K vs. $131K for on-site — a $19K gap. With 41% of postings flagged as remote, the supply of remote work remains strong.
Salary transparency is still below average
35% of postings included compensation data this month. The median was $128K, 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 65% of listings still leave candidates negotiating blind.
How This Report Is Built
Data transparency matters. Here's exactly what goes into these numbers.
Data Sources
Job postings are pulled from four sources: JSearch, Remotive, Adzuna, 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.
Scoring Methodology
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.
Ghost Job Signals
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.
Report Freshness
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.
Frequently Asked Questions
How is the fit score calculated?
What is a ghost job?
How often is this report updated?
What does L1–L5 skill depth mean?
Where does the salary data come from?
What job sources does ShouldApply pull from?
How do I see my own fit scores against these postings?
How is this different from LinkedIn's job match percentage?
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