Five real before/after About-section rewrites for data analysts and data scientists — the befores are representative data About sections from Standout's 2026 LinkedIn Visibility Benchmark (family median: 38/100), from completely blank to “finding the story in the numbers”, and every after is the opening of the About rewrite the live audit engine actually delivered for that profile.
Even with the benchmark-wide median of 38. Benchmark-wide, 60% of About sections contributed zero searchable keywords — one in five was completely blank. 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 Abouts bury the one thing every data recruiter searches first: the tools. In the benchmark's data cut, 6 of 10 profiles were missing SQL — the single most-searched data skill — from every section. The clichés fill the space instead: “detail-oriented”, “finding the story in the numbers”, “passionate about data-driven decisions”. Recruiters filter on SQL, Python, Tableau, dbt, Spark and the exact title; an About that names Excel and enthusiasm matches almost nothing.
Line 1–2: [level + exact target title] + [specialty] + [tools recruiters filter on] · then [one number that proves scope] · then the story
LinkedIn shows roughly the first three lines of your About before “…see more” — and weighs the field heavily in keyword search. So the opening sentences carry both jobs: put the exact title, the level, the specialty, and the searchable tools there, add one concrete number, and only then tell the story. Personality is fine — as the closer, not the opener. Every rewrite the engine returned below follows that order.
Each before is a representative data About section from the benchmark corpus, with the score it earned on September 18, 2026. Each after is the verbatim opening of the About rewrite the live audit engine delivered for that exact profile on September 25, 2026 — the full paste-ready section is what the $1 review ships.
(completely blank — the benchmark's most common About failure made concrete: nothing for recruiter search to index)
“About section is completely blank — the highest-weight keyword field recruiters index is empty.”
— the engine's About finding
I'm a Data Analyst who turns raw data into decisions using SQL, Python, and Tableau — building dashboards and analyses that give stakeholders answers fast. Whether it's cleaning…
Cut mid-sentence by design — the full paste-ready About rewrite is the $1 deliverable.
Keywords the engine flagged as missing — the raw material of the rewrite.
Detail-oriented analyst who loves finding the story in the numbers. Proficient in Excel and always learning new tools.
“'Detail-oriented analyst who loves finding the story' — clichés and only one tool named: Excel.”
— the engine's About finding
I'm a data analyst specializing in healthcare analytics, turning raw clinical and operational data into dashboards and reports that drive leadership decisions at Meridian Health Tech. From automat…
Cut mid-sentence by design — the full paste-ready About rewrite is the $1 deliverable.
Keywords the engine flagged as missing — the raw material of the rewrite.
Passionate about using data to drive decisions. I love machine learning, statistics, and telling stories with data. Lifelong learner on a mission to make data accessible.
“'Passionate,' 'lifelong learner,' and 'telling stories with data' contain zero recruiter-searchable terms or results.”
— the engine's About finding
Data Scientist specializing in machine learning, predictive modeling, and statistical analysis with Python and SQL. I build customer behavior models that turn raw data into…
Cut mid-sentence by design — the full paste-ready About rewrite is the $1 deliverable.
Keywords the engine flagged as missing — the raw material of the rewrite.
Recent data analytics bootcamp graduate excited to start my career in data. I've completed projects in SQL, Python, and Tableau, and I'm eager to bring my fresh perspective and work ethic to a great team.
“Opens with 'excited to start my career' — enthusiasm-first, keyword-last, with tools buried mid-paragraph.”
— the engine's About finding
I'm a Data Analyst skilled in SQL, Python, and Tableau, turning raw data into decisions — like my e-commerce capstone, where I analyzed sales data to surface revenue trends, and a…
Cut mid-sentence by design — the full paste-ready About rewrite is the $1 deliverable.
Keywords the engine flagged as missing — the raw material of the rewrite.
Data engineer with 5 years building pipelines and warehouses. Spark, Airflow, Kafka, AWS. I care about data quality, cost efficiency, and pipelines that don't page anyone at 3am.
“Opens with generic 'building pipelines and warehouses'; tools listed as bare comma string, no scale or seniority.”
— the engine's About finding
Senior Data Engineer with 5 years designing ETL/ELT pipelines and data warehouses on AWS — Spark, Kafka, Airflow, SQL. I've built streaming ingestion handling 50M events/day, cut wareh…
Cut mid-sentence by design — the full paste-ready About rewrite is the $1 deliverable.
Keywords the engine flagged as missing — the raw material of the rewrite.
Editorial patterns, not engine output: complete About-opener structures matching the shape of the rewrites above. Copy one, replace the [brackets] with your own titles, tools, and numbers — or let the engine write your whole About from your actual profile for $1.
[Level + exact target title] skilled in [SQL + 2–4 tools recruiters filter on: Python, Tableau, dbt, Spark]. At [company/context] I [build/own] [dashboards, pipelines, models] that [decision or outcome], including [one concrete artifact] serving [scale or audience].
SQL by name, in sentence one. It was missing from 6 of 10 data profiles in the benchmark — the family's single top failure.
I'm a [target title] specializing in [domain: healthcare analytics, product analytics, fraud]. I turn [raw input: clinical data, event streams] into [output: dashboards, models, experiment readouts] with [tools]. Proof: [metric win]; [scale number: 50M events/day, 600+ dbt models].
Domain + tools + one scale number — the exact structure of every rewrite the engine returned for this family.
[Target title] with [X] years across [pipelines / analytics / ML]. Stack: [SQL, Python, Airflow, Kafka] — woven into outcomes, not a bare list: [tool] powering [system] at [scale]; [tool] behind [win]. Currently [scope] at [company].
A comma-list of tools indexes, but doesn't persuade — the engine's flag on the strongest before in this set. Tie each tool to an outcome.
The sibling page: 6 real before/after headline rewrites for the same job family, from the same live engine.
The third deliverable: 5 real before/after bullet 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.
46% of headlines never name the level their target role requires — broken out by career stage.
About section 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 About sections 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 About rewrite opening the same live production engine — the exact code path behind every visitor's free audit — returned for that profile on September 25, 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: a rewritten About section like the ones above (the full section, tuned to your target role), a keyword-optimized headline, and your top 3 bullets reworded for recruiter search. One-time — no subscription.
Score my About section — freeNot sure whether to rewrite it yourself or pay a pro? See your LinkedIn profile review options compared — free, $1, and human services — and what human LinkedIn profile writers charge in 2026.