NH Job-Feed Metadata Errors: Salary & Education Mixups 2026

NH Hired is seeing a clear pattern in the statewide feed: a noticeable share of recent New Hampshire job listings contain inconsistent or plainly wrong education and salary metadata. In the last week alone we flagged multiple high-skill clinical and technical roles showing “High School” as the education level and a half-dozen six‑figure salaries paired with that same label. This isn’t a new hiring pattern — it’s a data problem that breaks filters, misleads candidates (especially recent grads), and erodes trust in aggregated job listings.

The trend is easy to spot in examples pulled from last week’s feed on NH Hired:

• Sonographer, Vascular and General — Salary: $173,325; Education: High School.
• X‑Ray CT Multimodality Tech — Salary: $319,292; Education: High School.
• Occupational Therapist — Salary: $136,491; Education: High School.
• Airway Transportation Systems Specialist — Salary: $101,536–$157,367; Education: High School.
• Staff Psychologist (BHIP) — Salary: $125,113–$162,649; Education: High School.
• Physician (Primary Care) — Salary: $255,000–$275,000; Education: Bachelors (likely should be MD).

Quick summary of what we counted and observed:

• At least six postings with salary >= $100K that list education as “High School,” which strongly suggests mislabeling rather than an unusual local hiring practice.
• Several postings with missing or null salary fields.
• A handful of remote/tech roles showing unexpectedly low or inconsistent education requirements compared with the job title.

Why this matters (and why you should care)

These metadata mismatches are more than cosmetic. They affect three things that matter to both job seekers and employers:

1. Search and filter accuracy — Job sites and aggregators rely on structured fields to let people filter by salary, education, or required license. When a CT tech is tagged as "High School," a mid-career clinician searching for allied-health roles with appropriate education will never find that posting, while someone filtering for entry-level jobs may get false hopes.

2. Candidate mismatch and wasted time — Applicants who respond to mis-tagged listings waste time applying to roles they’re not qualified for (or that are unrealistic), and employers get irrelevant applications. That makes hiring slower and more expensive.

3. Trust and platform integrity — Persistent outliers and obvious errors reduce confidence in an aggregator. Job seekers (especially graduates entering a tough market) may assume advertised wages and requirements are credible. Research referenced in recent reporting shows many postings don’t result in hires, and bad metadata feeds only heighten that skepticism.

What’s probably causing it

We can’t say for certain without auditing each feed, but the pattern points to a small set of usual suspects:

• Feed mapping errors: When external ATS/boards map their fields to an aggregator’s schema, picklist mismatches or index offsets can flip education fields or put salary text into the wrong column.
• Employer entry errors: Employers or small staffing firms may paste data in bulk or paste formatted documents that confuse parsers, leading to swapped or defaulted fields.
• Parsing and unit problems: Salary parsing can break when units are missing (annual vs hourly), ranges are formatted oddly, or currency symbols and commas are included in unexpected places.
• Default-value fallbacks: Some feeds use a default like “High School” when an education field is blank or unrecognized — that creates false data rather than null/unknown.
• Scrape noise and HTML markup: Scraped postings from websites with complex HTML structures can cause extraction tools to grab the wrong label.

Context from the broader NH market

The metadata problem arrives at a sensitive moment for New Hampshire’s labor market. State and regional reporting show employment softened in 2025 (about a 0.3% decline) and wages in several sectors lagged behind inflation. Local coverage has also highlighted that recent college graduates are facing a tougher job market; misleading job posts or inflated/incorrect salary information makes it harder for them to prioritize applications or evaluate realistic opportunities. Together, these forces increase the cost of bad metadata: candidates can be frustrated and employers may miss the right hires.

Practical next steps: How platforms and employers can fix and reduce these errors

The good news is most of these problems are fixable with a combination of short-term validation and longer-term process improvements. Below are concrete steps NH Hired and other platforms (and the employers feeding them) should take.

Technical fixes and immediate validations

• Add outlier detection for salary vs. occupation: Use occupation-to-salary baselines (state medians or O*NET/SOC references) to flag listings where the salary is, say, >3 standard deviations from the expected range for that occupation in NH.
• Cross-check education fields against occupation taxonomy: If an occupation typically requires a professional degree or license (e.g., physician, occupational therapist, sonographer), treat “High School” as a red flag that requires verification.
• Enforce normalized picklists and require units: Don’t accept free-text for education or salary without normalization. Require annual/hourly designation and allow only validated degree options.
• Treat blanks differently than defaults: Prefer null/unknown values over a default like “High School.” A null should show an “unspecified” badge rather than a plausible but incorrect label.
• Normalize incoming feeds at ingestion: Build a mapping layer per vendor that translates their fields into the site’s canonical schema and logs transformations for audit.

Operational and quality controls

• Sample manual verification: Regularly sample postings from each feed vendor for manual review. If a feed produces >X% errors in a sample, escalate and pause automatic imports until mappings are corrected.
• Vendor scorecarding: Maintain a simple quality dashboard per feed source showing rate of missing salaries, education mismatches, and outlier salary flags.
• Employer-facing UX changes: When employers post, provide better defaults, inline help, and validation (for example, when selecting job title = "Physician," require a license/degree field). Educate recruiters with tooltips showing typical education requirements.
• Add a flagged/verified indicator: Allow jobs to carry a “verified salary/education” flag after manual or automated checks. Users can filter for verified listings if they prefer.

Search and UI mitigations to protect job seekers today

• Surface warnings for potential metadata errors: If a job shows a high salary with a conflicting education tag, show a small notice like “salary or education looks unusual — details may be incorrect.”
• Improve filter behavior: Allow users to include/exclude listings with null or unverified metadata when filtering. Avoid falsely excluding accurate jobs that had bad parsing.
• Show raw posting text: Let users view the original posting content alongside parsed fields so they can judge for themselves whether the metadata is trustworthy.

Policy and longer-term data hygiene

• Standardize feeds with a minimal canonical schema: Require salary unit, min/max, education level picklist, and occupational code when onboarding new feed partners.
• Regularly retrain parsers: As employers change ATS or posting templates, scraping and parsing models need scheduled retraining or rule updates.
• Engage in employer education: Communicate common pitfalls to HR teams and staffing partners — how copy/paste and Excel uploads often break structured fields.

What employers should watch for

For HR teams and hiring managers: audit your ATS exports and check how your job posts appear on major aggregators. Small differences in CSV column order or an unexpected header can cause the entire mapping to shift, producing the exact errors we’re seeing. If candidates complain about confusing postings, it’s often a feed or export problem upstream rather than a site-specific bug.

For job seekers

If something looks off — unusually high pay for an entry-level tag, or a clinical role showing "High School" — treat the post cautiously. Look for posted licenses, read the job description for qualifications, and if possible check the employer’s career site to validate requirements before applying. When available, use the platform’s verified filters.

Final note

These metadata issues are fixable, and fixing them matters for the credibility of aggregated job markets in New Hampshire. NH Hired’s recent spot-checks found the examples above and we’re pushing feed partners and vendors to apply the kinds of fixes outlined here. Cleaner metadata improves search, reduces mismatches, and makes it easier for New Hampshire employers and job seekers to find each other in a market that’s already facing headwinds.