ATS Keywords for
Data Scientists
Data science titles cover an unusually wide range of jobs, so the screening keywords vary more here than in any other discipline. One requisition means causal inference and experimentation; another means training and shipping models; a third is analytics with a better title. Your CV is matched against whichever one you applied to. The terms below span that range, grouped by what they signal, so you can see which version of the role your current CV reads as.
14 ATS keywords for Data Scientist CVs
What each term signals to the system reading your CV.
Machine learning
The umbrella term nearly every requisition includes, and the baseline filter to clear.
Python (scikit-learn, pandas, NumPy)
Library names carry more signal than the language alone and are searched separately.
TensorFlow / PyTorch
Deep-learning framework names distinguish applied ML work from classical modelling.
Statistical modeling / regression
The foundation term for inference-heavy roles that are not about neural networks.
A/B testing / experimental design
Central to product data science, and screened as a hard requirement at consumer companies.
SQL
Every data scientist pulls their own data; the requisition assumes it and the filter checks it.
Feature engineering
Specific practitioner vocabulary that signals hands-on modelling rather than coursework.
Model deployment / MLOps
Splits research-only profiles from those who have shipped into production.
NLP / computer vision
Domain specialisations matched as their own strings when the posting needs them.
Data pipelines
Indicates you can source and maintain your own training data.
Hypothesis testing
The statistical rigour term used in research- and health-adjacent postings.
Big data (Spark)
Appears when the data does not fit in memory and is a hard requirement at scale.
Cross-validation / model evaluation
Evaluation vocabulary that shows methodological discipline, not just model fitting.
Business impact / ROI
Senior requisitions screen for commercial framing as much as for technical depth.
How to use this list
- Read the posting to work out which data science it means, then lead with that half — experimentation or production ML — in your summary.
- Report model results in the metric the domain uses (AUC, RMSE, precision at k) and then the business outcome it produced.
- If your models reached production, say so explicitly; "deployed" is a high-weight differentiator in ML screening.
- Keep the statistics vocabulary even on a deep-learning CV — many requisitions still filter on inference fundamentals.
Frequently asked questions
Everything you need to know before you upload.
Should I list Kaggle competitions or personal projects?
They help when your professional experience is thin, and they carry technique keywords honestly. Keep them in a clearly separate section so a reviewer never mistakes them for production work, which is what senior requisitions screen for.
How much does a PhD matter for the keyword screen?
Some research requisitions filter on the degree outright, in which case it is a literal requirement. For most industry roles it is one credential among several, and shipped models with measured impact weigh more.
Data scientist or machine learning engineer — which keywords do I use?
They are different filters. ML engineer postings weight deployment, serving infrastructure and software engineering practice; data scientist postings weight statistics and experimentation. If you target both, your CV needs both vocabularies clearly present.
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