These are the ATS keywords, credentials, and software terms that recur across current data scientist job descriptions — organized so you can scan for what your resume is missing. Placement matters as much as presence: an ATS keyword buried in a skills list ranks lower than the same term used in context inside an experience bullet.
Title variants
- Data Scientist
- Machine Learning Engineer
- ML Engineer
- Applied Scientist
- Research Scientist
- NLP Engineer
- Computer Vision Engineer
Core languages
- Python
- R
- SQL
- Scala
ML and frameworks
- scikit-learn
- PyTorch
- TensorFlow
- Keras
- XGBoost
- LightGBM
- Hugging Face
- Spark
- PySpark
Data tools
- pandas
- NumPy
- Jupyter
- Databricks
- BigQuery
- Redshift
- dbt
- Airflow
- MLflow
Methods
- machine learning
- deep learning
- natural language processing
- computer vision
- A/B testing
- statistical modelling
- feature engineering
- time series forecasting
- recommendation systems
- anomaly detection
Cloud and MLOps
- AWS SageMaker
- GCP Vertex AI
- Azure ML
- MLflow
- Weights & Biases
- Docker
Long-tail phrases
- data scientist resume examples
- how to write a data scientist resume
- data scientist cv template
- data scientist skills for resume
Pasting these terms into your resume verbatim without matching context can read as keyword stuffing to a human reviewer, even if it passes the ATS. The full Data Scientist resume guide shows where each category belongs and how to work it into real experience bullets.