Same career. Same facts. Two very different first impressions.
Your experience may already be strong. The question is whether a stranger can see it. LinkedCheck finds where the signal gets lost, then shows you what to change using evidence already in your career.
Nothing invented. Nothing added that you would have to defend in an interview.
Example profile · Marisol Vance
Same career. Better signal.
This sample shows the Complete LinkedIn Intelligence Report.
Experienced machine learning and data leader with over a decade of experience delivering data driven solutions for enterprise organisations. I am passionate about building high performing teams …see more
Responsible for data engineering across the analytics platform. Managed pipeline development, data quality and stakeholder relationships. Worked with product and operations teams…
“A manager at a company, with a degree.”
Most machine learning projects have a launch date. The harder part is what happens after launch. I build the systems, operating model and ownership around machine learning so they keep working …see more
- Cut the overnight feature pipeline run from six hours to under one.
- Introduced the retraining schedule the platform still runs on.
“The person who takes machine learning into production, and keeps it running.”
People decide whether to keep reading from the first few things they see. Completeness is 74 on both: the profile was already filled in. It just was not doing the job.
Twelve changes. Same career.
Each one starts with what a stranger misses today. Then the change itself, built from evidence already in her career.
01Headline
BeforeYour title says what you are. It doesn't say why someone should choose you.
Machine Learning Engineering Manager at Halden Group | MSc Data Science
AfterIn one line, the work you are known for becomes clear.
Your changeML Engineering Manager | Taking Machine Learning from Pilot to Production | MLOps & Data Platforms
Her employer and degree already appear elsewhere. The headline now uses its most valuable space to establish the work her career actually supports.
02About
BeforeTen years of experience reduced to three words everyone uses.
“Experienced machine learning and data leader… I am passionate about building high performing teams…”
AfterThe opening finally sounds like her career, not a template.
Your change“Most machine learning projects have a launch date. The harder part is what happens after launch. I build the systems, operating model and ownership around machine learning so they keep working after the project team moves on.”
Then the strongest fact on the profile, moved up from Experience: three models in daily payments use, each with a named owner.
03Experience
BeforeSix bullets explain what she was responsible for.
“Responsible for data engineering across the analytics platform. Managed pipeline development…”
AfterThree bullets prove what changed because she was there.
Your change6 hours to under 1 hour
Her overnight pipeline result was the fifth of six bullets. It now leads the role.
- Rebuilt the feature pipeline three forecasting models depended on, cutting the overnight run from six hours to under one.
- Introduced the retraining schedule the platform still runs on.
- Led the handover to the client operations team, including the runbook and the first two on-call rotations.
04Featured
BeforeHer strongest piece of evidence looks like a link nobody has a reason to click.
linkedin.com/posts/marisol-vance_7f3a… No title, no description.
AfterThe same work now tells the reader why it matters before they click.
Your change“What a real handover looks like: the runbook, the rota and the owner”
The same post and the same 312 reactions. The title now does the explaining.
In the Complete LinkedIn Intelligence Report
Beyond the profile: what her posts do, and who they reach.
How each of these is read: the content analyzer, the audience analyzer and the network analyzer.
05Content
BeforeHer feed mostly shows what other people are thinking.
AfterHer content starts building a point of view around the work she wants to own.
Your change- Posts in the last year
- 11
- Reshares with none of her own words
- 7
- Strongest post
- Production handover
- Write from the work: the handover checklist, what a retraining schedule really costs, what breaks three months after launch.
- Every reshare carries two lines of her own view.
06Engagement
Before14 reactions per post looks like the story. It isn't.
AfterOne post drew 312 reactions, and 41 people engaged with more than one. That is the audience to write for.
Your change- Production handover post
- 312 reactions
- Conference photos
- as few as 4
- People who engaged with more than one post
- 41
Do more of what brings the right people back, not simply what gets more reactions.
07Audience
BeforePlenty of people react. She cannot tell which of them responded to more than one post, or where they work.
AfterNow she knows who is already responding, how often, and where those people work, where available.
Your change- Most of the attention today
- People at Halden Group, and others who engage once
- Already appearing
- Senior technology and AI leaders, where their role is available
- People to notice
- Six who stand out on three or more measures at once, where their details are available
Every person here already engaged with her posts. Nobody is guessed from outside her audience.
Then the details that finish the story.
08Skills
BeforeThe skills at the top could belong to almost anyone in the field.
AfterThe skills reinforce the specific work she wants to be known for.
Your changeMachine Learning Operations · Production Machine Learning · Data Platform Architecture · Technical Handover
09Education
BeforeThe degree is listed, but it adds little to the story.
AfterOne relevant detail turns the credential into supporting evidence.
Your changeAdd one line: a dissertation on detecting drift in deployed forecasting models.
10Certifications
BeforeOlder and unrelated credentials compete with the expertise that matters now.
AfterThe credentials that support her current positioning move forward.
Your changeAWS Machine Learning Specialty (2024) and Google Professional ML Engineer (2023) go first. The two expired ones go.
11Recommendations
BeforeHer strongest third-party proof is buried halfway down the page.
AfterThe recommendation that proves her positioning becomes the first one people see.
Your change“It was still running the way she left it.”
Kestrel Systems CTO, now pinned first
Proves Taking Machine Learning from Pilot to Production
12Profile basics
BeforeA default banner, cropped photo and random URL make a strong career feel unfinished.
AfterThe first pixels now tell the same professional story as the rest of the profile.
Your changeA banner with one line on production ML, a clear headshot, and /in/marisolvance.
Nothing invented. Every line has a source.
A tool that writes a better-sounding profile gives you claims to defend. Every new line above comes from a position, a recommendation or a post already on Marisol's profile, so there is nothing to explain away in an interview.
“Three models from proof of concept into daily use”
Halden Group position, on the profile today“Cut the overnight run from six hours to under one”
Recommendation from her Brightline Data period“A permanent team ran it for two years without me”
Kestrel Systems recommendation: “still running the way she left it”
You already did the work. See whether your profile is showing it.
Start with the free read. Upgrade only if you want the prioritised changes, rewrites and intelligence behind them.
Free check · No card · Nothing is posted or changed on your profile