Will recruiters find you?

You paste in your headline, your About section and, if you like, one or two roles or projects from your experience. You choose the role you’re aiming for: Data Analyst, BI Analyst, Data Engineer or Data Scientist. Then we run the checks below in your browser.

Why these things? Recruiters search LinkedIn by job title and tools, and the results show your headline next to your name. Someone who opens your profile usually reads the first lines of your About and skims your experience for what you did and what came of it. So we check that the words people search for are there, and that your profile shows results rather than adjectives.

The score is out of 100:

• Headline (25 points). Names your target role (8), names two or three tools (7), says what you bring or the sector you want (4), fits in LinkedIn’s 220 characters (3), and avoids weak wording such as “aspiring”, “seeking opportunities”, “open to work” or only “Student at…” (3).
• About (30 points). 150 to 350 words (6), written in the first person (3), names your tools (5), mentions a project (4), results with numbers (5), what you’re looking for (3), how to reach you (2), and short paragraphs rather than one block (2).
• Experience and projects (20 points). Bullet lines (4) that start with an action verb (6), include a number or result (6) and name the tools you used (4). This part is optional: leave it blank and we score the other 80 points and scale the total to 100.
• Keywords for your role (15 points). The core keywords for the role you picked, found anywhere in your text. Finding three-quarters of them gets full marks; nice-to-have keywords count half.
• No empty buzzwords (5 points). You lose 2 points for each word like “passionate”, “hard-working”, “team player”, “results-driven”, “guru” or “ninja”. Soft words such as “detail-oriented” are fine when the same sentence proves it with a number or an example.
• Profile basics (5 points). Things we can’t see, which you tick: a profile photo (2), a custom profile URL (1), a Featured section with your work (1), and Open to Work set up with your target roles and locations (1).

A score of 70 or more means your profile covers what data profiles usually show. The result lists every check, the keywords found and missing, and a suggested headline built from your own role and tools using a common pattern (role, two or three tools, what you bring). It’s a starting point: edit it so every word is true.

Core keywords by role. Data Analyst: SQL, Excel, Power BI or Tableau, Python or R, dashboards, data cleaning, data visualisation, statistics, stakeholders, reporting, KPIs. BI Analyst: SQL, Power BI or Tableau, DAX, data modelling, dashboards, Excel, KPIs, stakeholders, reporting, Power Query or ETL. Data Engineer: SQL, Python, ETL or ELT, data pipelines, Airflow or another orchestrator, dbt, the cloud, a data warehouse, data modelling, Spark, Git. Data Scientist: Python, SQL, machine learning, statistics, pandas, scikit-learn, A/B testing, data visualisation, regression or classification, feature engineering. Only add the ones you genuinely have.

What it can’t do. These are simple word rules, so they can’t tell whether your writing is clear or your claims are true, and a line can tick a box without being good. We can’t see your photo, your skills section, your recommendations or your activity. It works best on profiles written in English.