Boolean search string for Data Scientist
Copy-paste strings to source Data Scientist candidates, plus the title synonyms, skills and exclusions they are built from. Adapt the terms to your market before running them.
LinkedIn search
("data scientist" OR "machine learning engineer" OR "applied scientist" OR "ml engineer" OR "research scientist") AND python AND sql AND "machine learning" AND (pandas OR "scikit-learn" OR pytorch OR xgboost OR "a/b testing" OR "causal inference" OR "feature engineering" OR statistics OR spark OR mlops OR mlflow OR airflow OR databricks OR dbt) NOT (recruiter OR staffing OR intern OR student)Paste into the LinkedIn people-search box. LinkedIn supports AND, OR, NOT and quoted phrases.
Google X-ray
site:linkedin.com/in ("data scientist" OR "machine learning engineer" OR "applied scientist" OR "ml engineer" OR "research scientist") python sql "machine learning" (pandas OR "scikit-learn" OR pytorch OR xgboost OR "a/b testing" OR "causal inference" OR "feature engineering" OR statistics OR spark OR mlops OR mlflow OR airflow OR databricks OR dbt) -recruiter -staffing -intern -studentPaste into Google. It searches public LinkedIn profiles without LinkedIn Recruiter — add a location in quotes to narrow it.
Terms behind the Data Scientist string
| Job titles | data scientist · machine learning engineer · applied scientist · ml engineer · research scientist |
|---|---|
| Core skills | python · sql · machine learning · pandas · scikit-learn · pytorch · xgboost · a/b testing · causal inference · feature engineering · statistics · spark |
| Nice to have | mlops · mlflow · airflow · databricks · dbt |
| Excluded | recruiter · staffing · intern · student |
Sourcing notes
- Start broad, then narrow. If a string returns under ~20 profiles, drop the nice-to-have group before you drop titles.
- Exclusions cut noise but also cut real candidates — check a few excluded profiles before trusting a NOT clause.
- Titles vary by company size: what reads as senior at a startup can be mid-level at an enterprise. Read the profile, not the label.
Build a custom Boolean string → · Interview scorecard for a Data Scientist →
Frequently asked questions
What is the difference between a data scientist and a data analyst when hiring?
A data scientist is hired for questions that need modeling or experimental design: causal estimates, predictions, A/B tests. An analyst is hired to make existing data answer business questions reliably and repeatedly. Interviewing both against the same rubric will mis-score at least one of them.
What should a data scientist technical interview include?
One experimental design problem, one modeling problem with a validation trap in it, and one case where the honest answer is that the data cannot support a conclusion. Add a walkthrough of a past project in the same session so you can compare stated method against actual practice.
How do I compare data science candidates fairly when their projects are all different?
Score the reasoning rather than the domain: framing, validation, uncertainty and handoff apply to any project. Fix the weights before the first interview and write the evidence beside each score. Verdict applies the same discipline to CVs, tying every dimension score to a cited snippet so candidates stay comparable.
When the stakes are a real hire, use evidence
These tools are quick heuristics. Verdict reads the CV against your job description and scores six dimensions with verbatim quotes as evidence — a hiring document you can defend.