Data Scientist Boolean Search String
A first-pass boolean search for sourcing data scientist candidates, built from the role's most-screened-for skills — adapt it with your own must-haves, or generate one tailored to your exact JD below.
"Data Scientist" AND "Statistical modeling" AND "Python/R" AND "SQL" NOT "recruiter" NOT "intern"site:linkedin.com/in/ "Data Scientist" "Statistical modeling" "Python/R" "SQL"Run this search on Google →How this string is built
The role title in quotes, ANDed with the top must-have skills for a data scientist — Statistical modeling, Python/R, SQL — each in quotes so LinkedIn and Google match the exact phrase rather than any word in it separately. NOT "recruiter" NOT "intern" filters out the two most common sources of false positives.
Other must-have skills worth adding
If the search above returns too many results, AND in more of these to narrow further:
- Machine learning fundamentals
- Data visualization/communication
- Deep learning framework experience (PyTorch/TensorFlow)
- A/B testing experience
- MLOps/model-deployment experience
Other titles to search for a data scientist
The same job is also called Machine Learning Engineer, Applied Scientist, Data Analyst (Advanced), Research Scientist. Searching only for “Data Scientist” misses those people, so join the titles with OR inside brackets and AND the skills after them.
Five searches to run, from broad to narrow
- Broad: every title, one core skill
("Data Scientist" OR "Machine Learning Engineer" OR "Applied Scientist" OR "Data Analyst (Advanced)" OR "Research Scientist") AND "Statistical modeling" - Focused: three skills, with the noise removed
("Data Scientist" OR "Machine Learning Engineer" OR "Applied Scientist") AND "Statistical modeling" AND "Python/R" AND "SQL" NOT "data entry" NOT "business analyst" NOT "intern" - Tools and credentials: the nice-to-haves as an OR group
("Data Scientist" OR "Machine Learning Engineer" OR "Applied Scientist") AND ("Deep learning framework experience (PyTorch/TensorFlow)" OR "A/B testing experience" OR "MLOps/model-deployment experience") - Google X-ray on LinkedIn profiles
site:linkedin.com/in/ ("Data Scientist" OR "Machine Learning Engineer" OR "Applied Scientist") "Statistical modeling" "Python/R" - Feeder role: people from a neighbouring job
"Data Analyst" AND "Statistical modeling" AND "Python/R"
Start with the broad search to see how large the pool is. If it is too big, move to the focused one. If it is too small, drop a skill or use the feeder search.
Noise to exclude for this role
For a data scientist the usual false positives are data entry, business analyst, intern. Each one added with NOT removes a group of profiles that look similar but are not the job.
Where else to look
Good data scientist candidates often come from Data Analyst, Statistician, Software Engineer. Search for them with the same core skills, then check that they are ready to move.
What to check on the profile
Models shipped, the measured impact and the data size worked with.
Credentials to confirm
A quantitative degree is common. Ask for work that reached production.
Frequently asked questions
Why AND the skills together instead of OR?
AND narrows to candidates who show evidence of every listed skill — good for a first-pass search on a specific role. If that returns too few results, dropping to OR between the least-critical skills (keeping the must-haves ANDed) widens the pool without losing precision on what actually matters.
Why exclude "recruiter" and "intern"?
Those two terms are the most common source of false positives in a data scientist search — recruiters and internship-seekers whose profiles happen to mention the same skill keywords. Adjust or remove the exclusion if it's cutting out candidates you actually want (e.g. if you're specifically hiring for an internship).
How is this different from the full Boolean Search Generator tool?
This is a static starting-point example built from data scientist's most common requirements. The tool builds a tailored version from your actual job description — different seniority, location, and specific must-haves than this generic example.
What other job titles should I search for a data scientist?
The same job is also called Machine Learning Engineer, Applied Scientist, Data Analyst (Advanced), Research Scientist. Put them in a bracketed OR group so one search covers them all, for example ("Data Scientist" OR "Machine Learning Engineer" OR "Applied Scientist").
What should I exclude when sourcing a data scientist?
The usual noise is data entry, business analyst, intern. Add each with NOT so the results are people who do the job, not people near it.
Where else can I find data scientist candidates?
Look at people in related roles who already have the core skills: Data Analyst, Statistician, Software Engineer. Search for them with the same skills and check whether they are ready to move across.
Hiring a data scientist? The rest of the toolkit
- How to write the job description →
Titles, must-haves, credentials and a starting posting.
- Interview questions to ask →
Role-specific questions and what a good answer contains.
- Run the whole hire in one place →
Jobs, CV review, interviews, offers and checks together.