Technology Resume Example & Guide

Data Scientist Resume Example
that gets interviews in 2026.

Data scientists build models and experiments that drive product and business decisions. Hiring managers look for the full loop — framing a problem, building a model, and shipping it into a decision that moved a number.

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Bullet points that work

Data Scientist resume bullet examples

Strong data scientist bullets lead with a verb and end with a number. Use these as patterns — swap in your own tools, scope, and results.

Skills recruiters scan for

Top data scientist skills for your resume

Hard skills

PythonSQLMachine learningscikit-learnTensorFlow / PyTorchStatisticsExperimentation / A/B testingFeature engineeringMLOpsSparkData pipelines

Soft skills

Scientific rigorCommunicating uncertaintyCross-functional partnershipPrioritization

ATS keywords to include

Mirror the exact wording from the job post. These terms are commonly matched by Applicant Tracking Systems for data scientist roles:

data scientistmachine learningpredictive modelingstatistical modelingexperimentationnlpmodel deployment

Strong action verbs

ModeledTrainedDeployedExperimentedImprovedProductionizedValidated
Pay & outlook

Data Scientist salary & job outlook

$112,590
U.S. median annual wage
much faster than average
Projected job growth

Source: U.S. Bureau of Labor Statistics, OEWS & Employment Projections (May 2025 release). Median pay varies by state and metro — see your state's page below.

Related roles

Other technology resume examples

Build a data scientist resume in your state

Common questions

FAQ

What separates a data scientist resume from a data analyst one?
Modeling and experimentation. Analysts describe what happened; scientists build the model or test that predicts or causes what happens next. Lead with deployed models and measured lift.
Do I need a PhD on my resume?
No. List the highest relevant degree, but a portfolio of shipped models and clear business impact outweighs credentials for most industry roles.
Should I list Kaggle rankings?
Only if they are strong (top tier) and you have limited industry experience. Otherwise prioritize production work that affected real metrics.

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