Five real before/after experience-bullet rewrites for data analysts, scientists, and engineers — the befores are representative data bullet lists from Standout's 2026 LinkedIn Visibility Benchmark (family median: 38/100), from no bullets at all to “responded to ad hoc data requests”, and every after is the opening of the first reworded bullet the live audit engine actually delivered for that profile.
Even with the benchmark-wide median of 38. Benchmark-wide, half of all profiles were flagged for duty-based, metric-free bullets — and another 14% had no usable bullets at all. Full distribution and failure analysis in the data cut.
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.
Data bullets fail in two directions. The junior half lists chores — “cleaned and validated data”, “responded to ad hoc requests”, “maintained Excel dashboards” — with no tools, no scale, no decision anyone made because of the work. The senior half has real numbers (50M events/day, build time −55%) but skips the terms recruiter searches filter on: SQL, dbt, ELT, data modeling, ML technique names. The engine's rewrites always carry all three: the tool, the scale, and what the business did with the result.
[action verb that signals ownership] + [keywords recruiters search: tools, segment, discipline] + [one number that proves the outcome]
LinkedIn's search matches the literal text of your experience bullets, and recruiters skim them in seconds. So each bullet has two jobs at once: carry the exact terms a search filters on (the stack, the segment, the discipline), and prove the work moved a number (latency, pipeline, activation, spend). Duties do neither — “Responsible for…” bullets were flagged on half the benchmark. Every rewrite the engine returned below opens with the verb, keeps the keywords, and lands on the number.
Each before is a representative data bullet list from the benchmark corpus, with the score it earned on September 18, 2026. Each after is the verbatim opening of the first reworded bullet the live audit engine delivered for that exact profile on September 26, 2026 — the full top-3 bullet rewrite is what the $1 review ships.
(no experience bullets at all — the benchmark's “no usable bullets” failure: nothing for recruiter search to match, no proof of scope for a recruiter who does land here)
“No experience bullets shared — recruiters see zero proof of skills or outcomes.”
— the engine's Experience finding
• Built automated SQL + Tableau dashboards tracking core KPIs, cutting weekly reporting time 60% and giving…
Cut mid-phrase by design — the full 3-bullet rewrite (your top 3 bullets reworded for recruiter search) is the $1 deliverable.
Keywords the engine flagged as missing — the raw material of the rewrite.
“'Created weekly and monthly reports' and 'Responded to ad hoc requests' read as tasks with no outcomes or tools.”
— the engine's Experience finding
• Built and automated recurring executive reporting in Excel dashboards for leadership, cutting weekly prep time and…
Cut mid-phrase by design — the full 3-bullet rewrite (your top 3 bullets reworded for recruiter search) is the $1 deliverable.
Keywords the engine flagged as missing — the raw material of the rewrite.
“'Built models to analyze customer behavior' names no algorithm, tool, or measurable outcome.”
— the engine's Experience finding
• Developed customer behavior prediction models in Python (scikit-learn, pandas), improving segmentation…
Cut mid-phrase by design — the full 3-bullet rewrite (your top 3 bullets reworded for recruiter search) is the $1 deliverable.
Keywords the engine flagged as missing — the raw material of the rewrite.
“'Completed capstone project analyzing e-commerce sales data' has no metrics, tools, or business outcome.”
— the engine's Experience finding
• Analyzed 12+ months of e-commerce sales data using SQL and Python, uncovering revenue trends that inform…
Cut mid-phrase by design — the full 3-bullet rewrite (your top 3 bullets reworded for recruiter search) is the $1 deliverable.
Keywords the engine flagged as missing — the raw material of the rewrite.
“Bullets like 'Built streaming ingestion pipeline' lack terms recruiters filter on, e.g. 'data platform', 'ELT'.”
— the engine's Experience finding
• Engineered real-time streaming data pipeline (Kafka, Spark on AWS) ingesting 50M+ events/day with…
Cut mid-phrase by design — the full 3-bullet rewrite (your top 3 bullets reworded for recruiter search) is the $1 deliverable.
Keywords the engine flagged as missing — the raw material of the rewrite.
Editorial patterns, not engine output: complete bullet structures matching the shape of the rewrites above. Copy one, replace the [brackets] with your own verbs, tools, and numbers — or let the engine reword your actual top 3 bullets from your real profile for $1.
[Built/Automated] [artifact: SQL + Tableau dashboards, dbt models, reporting layer] tracking [what: core KPIs, revenue metrics] for [audience: leadership, 200+ weekly viewers], cutting [time/cost metric] [X%].
Tool + audience + saved-time number — turns “maintained dashboards” into a result recruiters can both search and believe.
[Developed/Deployed] [model/analysis type: churn prediction, CUPED, segmentation] in [tools: Python (scikit-learn), SQL], [business outcome: lifting retention X%, informing $XM pricing decision, cutting false positives 30%].
Naming the technique AND the library is what separates a searchable data-science bullet from “built models” — the exact flag on the profile above.
Engineered [pipeline/platform: streaming ingestion, ELT, metrics layer] on [stack: Kafka, Snowflake, Airflow, dbt] handling [scale: 50M events/day, 600+ models], [reliability/cost outcome: compute spend −30%, incident rate −60%].
The mid-tier engineer above had every number and still got flagged — “data platform”, “ELT”, and the stack list are the search terms that make the numbers findable.
6 real before/after headline rewrites for the same job family, from the same live engine.
5 real before/after About-section rewrites for the same job family, from the same live engine.
All 10 data scores, the family's top failure mode, and how it compares to the other four families.
All 50 profiles: median 38/100, 78% under 50, the top-3 failure modes, full methodology.
Experience bullet examples for the other families: Software Engineers · Product Managers · Sales Professionals · Marketers · Finance Professionals · Operations Managers · Product Designers · Customer Success Managers · Healthcare Professionals
The before bullets are from Standout's 2026 LinkedIn Visibility Benchmark corpus — representative profiles, modeled on common real-world LinkedIn patterns: constructed and fictional (invented names and companies), never scraped, because LinkedIn blocks anonymous profile access and Standout's own product promise is no account access. Scores are from the benchmark run on September 18, 2026; each after is the verbatim bullet rewrite opening the same live production engine — the exact code path behind every visitor's free audit — returned for that profile on September 26, 2026. Full methodology and limitations are in the complete report; raw data is available to any journalist via [email protected].
Patterns are a start; the engine is better with your real material. Type your name — or paste your LinkedIn URL — and get your 0–100 recruiter-visibility score free in 60 seconds, no sign-in, no account access. $1 unlocks exactly 3 paste-ready rewrites: your top 3 experience bullets reworded for recruiter search like the ones above (all three, in full, tuned to your target role), a keyword-optimized headline, and a rewritten About section. One-time — no subscription.
Score my bullets — freeWeighing DIY against hiring a writer? Compare your LinkedIn profile review options honestly — or check the going rate for a LinkedIn profile writing service ($99–$500+).