ATS keywords for Data Scientist
This role is usually screened by a data/ML lead (sometimes a tech recruiter) who needs to see fast whether you can combine programming, statistics and machine learning to solve business problems. The ATS filters on exact terms like Python, SQL and Machine Learning before a human reads anything — so the right keywords, tied to results, decide whether your resume gets through.
Essentials
High frequency in postings — the ATS expects them.- Python
the role's primary language
- SQL
query and extract data
- Machine Learning
models that learn from data
- Estatística
hypothesis tests, distributions, inference
- Modelagem preditiva
classification, regression, forecasting
- scikit-learn
standard ML library
- pandas
data wrangling and cleaning
- NumPy
numerical computing in Python
- A/B testing
experimentation and reading results
- Data visualization
translate analysis for the business
- Git
code version control
- Stakeholders
communicate insights clearly
- Cloud (AWS/GCP)
data and model environment
Differentiators
Set more competitive candidates apart.- Deep Learning
neural nets, CNNs, RNNs
- TensorFlow / PyTorch
deep learning frameworks
- MLOps
put models into production
- Feature engineering
build variables that improve models
- Docker
package and deploy models
- BigQuery
cloud data warehouse
- Power BI / Tableau
dashboards for the business
- NLP / LLMs
text, GenAI, RAG
- Airflow
pipeline orchestration
- Spark
big data processing
How to use them without overdoing it
- 1Use the job's exact term: if it says 'predictive modeling', don't swap in 'advanced analytics' — the ATS matches word for word.
- 2Anchor each keyword to a result: 'churn model in Python/scikit-learn that cut attrition 12%', not a loose list of tools.
- 3Weave keywords into real prose (summary, experience) instead of stacking them in a 'skills' block — context-free repetition reads as stuffing.
Find out which of these you already have
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