Data Scientist resume keywords
Most data scientist applications are screened by software before a person reads them: the ATS ranks resumes by how many of the posting's words they contain. The lists below are the terms that show up most in data scientist job ads. Use the ones that are true for you, with the same spelling the ad uses.
Hard skills and ATS keywords
- Python
- SQL
- scikit-learn
- XGBoost
- A/B testing
- Statistics
- Airflow
- Stakeholder communication
- Machine learning
- Statistical modelling
- Feature engineering
- Pandas
- Spark
- Deep learning
- Model deployment
- Causal inference
- Data visualization
- Cloud (AWS / GCP)
Soft skills recruiters look for
- Business impact focus
- Communication
- Curiosity
- Rigour
- Collaboration
Soft skills only count when a bullet proves them. "Team player" is ignored; "Built a churn model (AUC 0.86) that targeted retention offers and saved $1.2M a year." shows it.
Action verbs to start your bullets
- Built
- Deployed
- Modelled
- Improved
- Designed
- Predicted
- Reduced
- Automated
How to use these keywords
- Mirror the job ad: if it says "Machine learning", do not write a synonym.
- Put the top 6–8 in a skills section and repeat the key ones inside experience bullets with a number.
- Never list a skill you cannot talk about in an interview.
- Keep the file text-based (no skills inside images or tables), or the ATS cannot read it.
Which ones is your resume missing?
Every posting is different. Paste your resume and the job ad — free ATS Match Score Score your resume free (0-100)
No signup. It lists the keywords from the ad that your resume does not contain.
Need the resume itself? See the data scientist resume example and generate one free from rough notes.