Ask a data scientist about a model and the useful follow-up is some version of "better than what?" A LinkedIn About section that never answers it tends to read as a stack list. Python, SQL, scikit-learn, PyTorch, "machine learning, deep learning, NLP and statistical modeling." A recruiter reading that still cannot tell whether you build forecasting models for a supply chain team, run causal analyses for a product org, or fine-tune language models for a search team. Those are three different hires.
A strong data scientist LinkedIn About section names the problem you model, shows one model that beat a stated baseline and changed a decision, and closes with the domain and stack a recruiter will search for. LinkedIn's help guidance wants the section to cover mission, motivation and skills inside one or two paragraphs, enough room for those three parts and little else. The six templates below follow the shape. Fill them only with work you can defend in a technical screen, because a likely first question about any model you name is "what was the baseline?"
What should a data scientist put in a LinkedIn About section?
Lead with the kind of problem you model, then prove one model worked, then name where you did it. Those three parts answer most of what a recruiter wants to know before sending a message.
Your specialization. "Data scientist" covers product and decision science, applied machine learning, research, NLP and analytics-heavy roles, and each has a different interview loop. The US Bureau of Labor Statistics lists one of the job's core duties as to "make business recommendations to stakeholders based on data analysis," so the work often ends with someone deciding something. Say which decisions your work feeds.
Your evaluation. Name the metric you optimized and what you compared the model against. "Built a churn model" is a task. "Built a churn model that the retention team now uses to choose who gets an offer, and checked it against the rule they used before" is evidence, and it hands an interviewer a good first question.
Your context. Name your industry, your data sources and whether your models run in production or inform analyses. Someone hiring for fraud modeling is likely to search for "fraud," "transactions" or "anomaly detection," so use the words the postings use.
How is a data scientist About section different from a data analyst's?
A data scientist About section has to say what happened after the model, which a data analyst's rarely needs to. An analyst's strongest line is usually the question they answered and the decision it changed. That pattern is covered in Four-Leaf's data analyst About section examples. A data scientist adds two things on top of it.
The model's job. Say what the model predicts, ranks or estimates, and who consumes the output. A forecast read by planners, a ranking model served to users and a causal estimate shown to leadership are different kinds of work.
How you knew it worked. Offline accuracy alone is a weak claim, because most candidates can show a model with good offline numbers. The stronger claim is that you tested it against the existing approach, in an A/B test, a backtest or a holdout, and said what you would have done if it had lost. Use your real figure if you measured one, and describe the comparison in words if you did not. A rounded-up number in a data scientist's profile is a particular risk, because the interviewer is likely to ask how it was calculated.
What does a strong data scientist About section look like?
The six templates below are grouped by specialization and written in first person. Swap each bracket for a fact from your own work, and drop any line you could not back up in a screen.
Product or decision scientist
I help product teams decide what to ship by measuring what their changes actually do. I work with [type of] product teams at [company type], and most of my week goes to experiment design, causal analysis and the conversations about what a result does and does not show. Before [team] shipped [proposed change], I [designed the test or built the analysis] and found [finding], so they [what they did]. My tools are SQL and [Python or R], and I say what a result cannot tell us as plainly as what it can.
Applied machine learning scientist
I build models that run in production and get judged by the product metric they move. My focus is [ranking, recommendations, forecasting, fraud or another problem] for [type of product or business]. At [company], I [built or replaced which model] and tested it against [baseline] in [an A/B test or a backtest], and [result]. I own the work from feature design through [deployment and monitoring], in [Python, your framework and your platform].
Forecasting and operations data scientist
I forecast what a business needs before it needs it. I work with [demand, inventory, staffing or capacity] data at [type of organization], and my models feed the plans other teams commit to. On [project], I [built which forecast] for [team], replacing [previous method]. The planners [what they changed]. I report error bars with every forecast and work in [Python or R] with [tools].
NLP or language model data scientist
I work on [search, classification, extraction, summarization or another task] over text, which these days means deciding when a language model is the right tool and when a simpler model is enough. At [company], I [built, fine-tuned or evaluated what] for [use case], with an evaluation set built from [source]. The result [what changed for users or the team]. I work in [Python, your framework and your model stack].
Career changer or new graduate
I came to data science from [previous field or degree], where I [what you did with data or quantitative methods]. I am looking for my first data scientist role on a team working on [problem area]. In [capstone, thesis or portfolio project], I asked [question] using [dataset], built [model], and compared it against [baseline]. I [completed or am completing] the [certificate or degree]. The code and write-up are in my Featured section below.
Research or senior data scientist
I lead modeling work on [problem area] for [team or company], and a large part of that is telling teams when a simpler rule will do. My background is [PhD field or years of industry work in a domain]. On [initiative], I [what you led], which [outcome]. I also [mentoring, review standards, publications or tooling you introduced]. I work across [stack] depending on what the problem needs.
Which certifications should a data scientist name?
List a certification only if you have earned it or are partway through it, and use its full official name, which is what a recruiter will search. In Four-Leaf's view, shipped work says more about a data scientist than any certificate. The Bureau of Labor Statistics notes that some employers require or prefer that applicants have a master's or doctoral degree. Certifications matter most in two places, which sit at different stages of a career.
Entry-level programs. The IBM Data Science Professional Certificate on Coursera says "no prior experience required" and estimates 4 months at 10 hours a week. On a profile it signals foundational training, which helps a career changer who has no data title yet. Pair it with a project of your own, because the certificate says what you studied and a project says what you chose to build.
Cloud machine learning credentials. Google Cloud's Professional Machine Learning Engineer certification lists 3+ years of recommended industry experience and a validity period of 2 years. AWS describes the ideal candidate for its Certified Machine Learning Engineer Associate as having at least 1 year of experience in machine learning engineering or a related field, and says the certification is valid for 3 years. Either one is most relevant to applied ML roles on that platform. Neither replaces a project that shows the same work.
Check a credential's status before you build a sentence around it. Microsoft's page for the Azure Data Scientist Associate says the certification and its renewal assessment are retired. If you hold an older credential like that one, keep it in the Licenses and certifications section and lead with your work instead.
What is overrated
The algorithm list. "Experienced in regression, random forests, XGBoost, neural networks, NLP and computer vision" describes a course syllabus. The reader cannot tell which of those you used on a real problem. Name the method that carried your best project, in the sentence about it.
Kaggle rankings as the headline achievement. A competition result shows you can optimize a fixed metric on prepared data. Many data science jobs are harder at the other end, choosing the metric and getting the data, so put a strong result after the work that shows that.
"Passionate about AI." Many profiles say some version of it. Name the problem you want to work on instead, which gives a recruiter a reason to message you.
Model accuracy with no baseline. "Achieved high accuracy" means nothing without a comparison, and an interviewer is likely to treat an unanchored figure as a weakness. Compare against the method the team used before, or leave the number out.
Rewrite your About section this week
- Write one sentence naming the kind of problem you model and who uses the output.
- Pick the project you would most like to discuss in a technical screen and write two sentences covering the baseline, the comparison and the decision that followed.
- Pull three job postings for the specialization you want and copy their exact tool and method names into your second paragraph.
- Feature the write-up of your lead project. LinkedIn's help page notes you can add media samples to provide quick access to your work portfolio, and a readable write-up with the baseline in it does more than the repository alone.
- Check the draft on your phone. If it runs past one or two paragraphs, cut the stack list first.
- Run it through Four-Leaf's LinkedIn optimizer, which grades your About section for the target role saved in your Four-Leaf profile and drafts a version to edit. Verify each line of that draft against the project it describes, and trim it back to one or two paragraphs if it grew.
- Prepare the technical walk-through of your lead project with the data science interview preparation guide, because the screen will ask about it.
The LinkedIn profile optimization guide covers the rest of the profile.
Where this is heading
Writing "machine learning" on a profile costs nothing, which is why it tells a reader so little. What still separates a data scientist is evidence that their models were tested against something and changed what a team did. The About section is the one place on your profile where you can show that in your own words, and the version that names a baseline will be the one an interviewer remembers.
Originally published on the Four-Leaf blog.
