Monthly Market Report
November 2026 Job Market
65
postings scored across 39 companies
Fit scores, skill demand, salary transparency, ghost job analysis.
Key Metrics
65
Jobs Analyzed
↓ 5490 vs October
39
Companies Hiring
unique employers
46
Avg Fit Score
↑ 6pts vs October
32%
Salary Disclosed
21 listings
55%
Remote Listings
of all postings
5%
Ghost Signal Rate
of postings
Executive Summary
The scoring engine processed 65 job postings in November 2026, running them against active candidate profiles to generate 65 fit scores. That's down from 5,555 the prior month. The average fit score was 46 — 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 up 6 points from last month's 40. 8 postings (12%) crossed the 75-point threshold — the range where a application is worth serious effort. 43 postings (66%) scored below 60, meaning the fit was too thin to compete without significant profile improvement.
On the demand side, Customer Service, Attention to detail, Bachelor’s degree led across all scored postings. Technical Support Experience and Bachelor’s degree led the gap list — appearing in dozens of postings where candidates consistently fell short of the required depth. These aren't obscure skills. A 80% gap rate across 5 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 32% of listings, which is workable but limits precision on the compensation side. 55% of postings were listed as remote or remote-friendly. Ghost job signals were relatively low at 5%, 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.
Fit Score Distribution
How scored postings spread across the 0–100 range. Scores below 60 represent thin fit; 75+ is where applications compete well.
43
Below 60 (weak fit)
14
60–74 (partial fit)
8
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 — November 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.
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 |
|---|---|---|
| Technical Support Experience | 80% gap | 5 |
| Bachelor’s degree | 50% gap | 6 |
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 21 postings that disclosed compensation out of 65 total (32% transparency rate). Midpoint is used where both min and max are listed.
$181K
Median Salary
$179K
Average Salary
$58K
Floor
$325K
Ceiling
Remote vs. On-Site Compensation
$198K
Average — On-site listings
Salary by Role Category
Roles with at least 3 salary-disclosing postings
| Role | Median | n |
|---|---|---|
| Software Engineering | $185K | 15 |
| Other | $73K | 4 |
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.
5%
of postings with ghost signals
3
postings with at least one ghost signal
62
postings with clean quality signals
5% of Scored Postings Flaggable for Ghost Signals
Across 65 listings analyzed during November 2026, our engine evaluated four primary low-intent signals before candidates invest time tailoring resumes.
Stale Postings (>45 Days)
Listings remaining active well past standard hiring cycles.
Zero Pay Disclosure
No base salary floor or range stated in the listing.
Evergreen / Reposted Pools
Continuous talent-pool titles or recurring duplicate requisitions.
Vague / Thin Requirements
Generic job descriptions lacking verifiable team milestones.
“According to ShouldApply's November 2026 Hiring Report, 5% of tech job postings carried active ghost signals, with 51% disclosing zero salary and 32% sitting open for over 45 days. Source: https://shouldapply.com/reports/2026-11”
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 — November 2026
Patterns worth noting from this month's dataset. Not statistical projections — just what the numbers show.
Depth requirements are rising in specific areas
Customer Service, Attention to detail, Python averaged 4.7+ 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. Customer Service skill profile →
By contrast, Technical Support Experience and Network Troubleshooting appeared frequently but at low depth (L1), which means they're table stakes — worth having, but not differentiating.
Salary transparency is still below average
32% of postings included compensation data this month. The median was $181K, 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 68% 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 three sources: JSearch, Remotive, 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?
See how you stack up
Stop guessing. Score your fit against real postings.
Upload your resume, get scored against 65 postings from November, and see exactly where you match and where you don't.