Ten representative data profiles — analysts, engineers, and scientists modeled on the most common real-world patterns — scored by Standout's live audit engine. Median: 38/100, with the highest mean of the five families (42.2) and the same fatal gap top to bottom: unnamed tools.
The highest mean of the five families — undone by profiles that never name SQL, Python, or the tools recruiters filter on.
Type your name — or paste your LinkedIn URL — and get your 0–100 recruiter-visibility score free, right on this page. No sign-in, no account access.
median — even with the benchmark-wide median of 38
mean score across the 10 profiles
scored below 50/100
full score range, weakest to strongest
the family's ceiling — its strongest profile's score
7 of 10 data profiles scored below 50 — the fewest of any family, which still means most data profiles fail. The seniority gap is the other repeat flag: three senior-targeting profiles (data-07, data-08, data-09) never said “Senior” anywhere, so seniority-filtered searches skip them outright.
Counted from the engine's own findings across the family's 10 audits — each audit returns one finding per area: headline, About, experience.
Data hiring runs on tool filters — SQL, Python, Tableau, Power BI, dbt — and the benchmark's data profiles simply don't name them. 6 of 10 were missing SQL, the single most-searched data skill, from every section the engine could see. The failure spans the whole quality range: sparse profiles name no tools at all (“Analyst at Crestline Insurance”), mid profiles bury them in a bare comma list, and even the family's top scorer — a genuinely strong senior data scientist at 66 — wrote bullets that name zero tools.
“'Analyst at Crestline Insurance' names your employer, not your skills, tools, or the 'Senior Data Analyst' title.”
— the engine on data-02, scored 28
“'Turning Data Into Insights' is a cliché that no recruiter ever types into search.”
— the engine on data-04, scored 38
“Bullets like 'Introduced CUPED and sequential testing' name zero tools — no SQL, Python, or ML anywhere.”
— the engine on data-10, the top data scorer at 66
See the fix in practice: 6 real before/after headline rewrites for data → and 5 real before/after About-section rewrites → and 5 real before/after experience-bullet rewrites → The site-wide top-3 failure modes — headline, About, and experience-bullet failures across all 50 profiles — are in the full benchmark report.
No job family is meaningfully “better at LinkedIn” — all five means land within 3 points of each other. Each family has its own cut of the data.
| Family | n | Mean | Median | Range | Cut |
|---|---|---|---|---|---|
| Engineering | 10 | 40.1 | 34 | 22–71 | Read cut → |
| Product | 10 | 41.9 | 38 | 24–66 | Read cut → |
| Sales | 10 | 39.2 | 38 | 19–66 | Read cut → |
| Marketing | 10 | 41 | 41 | 18–68 | Read cut → |
| DataTHIS PAGE | 10 | 42.2 | 38 | 22–66 | — |
Full distribution, tier sanity check, and all 50 per-profile scores: the complete 2026 benchmark report →
The profiles are constructed, not scraped. This cut covers the 10 data profiles from the benchmark's 50 — each a fictional profile, with invented names, slugs, and companies, modeled on the most common real-world LinkedIn patterns, across a deliberately realistic quality spread per family (2 sparse, 4 typical-weak, 2 mid, 1 decent, 1 strong). LinkedIn blocks anonymous profile access, and Standout's own product promise is no account access, no scraping — so a scraped corpus is neither possible nor honest. Each profile was scored in a single pass by the live production audit engine — the exact same code path behind every visitor's free audit on this site — on September 18, 2026; every score is the first and only result returned.
When citing, say “representative LinkedIn profiles” or “profiles modeled on common real-world patterns” — never “real profiles.” Full methodology and limitations are in the complete report. Raw per-profile data, the corpus, and the runner scripts are available to any journalist on request via [email protected].
“In a September 2026 benchmark by Standout (standout9.acoco.ai), 10 representative data LinkedIn profiles — modeled on common real-world patterns — scored a median of 38/100 for recruiter-search visibility. 6 of 10 were missing SQL, the single most-searched data skill, from every section.”
“In a September 2026 benchmark by Standout (standout9.acoco.ai), 50 representative LinkedIn profiles across engineering, product, sales, marketing, and data scored a median of 38/100 for recruiter-search visibility — 9 in 10 scored below 60, and 42% of headlines never named the job the person was actually targeting.”
The benchmark used the exact engine behind the free audit. Type your name — or paste your LinkedIn URL — and get your own 0–100 recruiter-visibility score in 60 seconds. Free, no sign-in, no account access. If you want the fixes, $1 unlocks exactly 3 paste-ready rewrites: a keyword-optimized headline, a rewritten About section, and your top 3 bullets reworded for recruiter search. One-time — no subscription.
Get my free scoreScored below the median and deciding what to do about it? Compare every LinkedIn profile review option — free vs. $1 vs. $400 or see what human profile writers charge in 2026 ($99–$500+).