Data Scientist Resume Keywords (Technical Skills Keywords + ATS Examples)
This guide groups technical skills the way strong job descriptions do: core modeling, data manipulation, experimentation, and production touchpoints. Mirror the posting’s taxonomy and prove depth with how you applied each skill—not a flat keyword list.
Modeling & statistics
Classical and modern ML techniques recruiters expect to see spelled out.
- classification
- regression
- gradient boosting
- logistic regression
- regularization
- cross-validation
- hyperparameter tuning
- probability calibration
- time series
- survival analysis
Weak: Machine learning algorithms
Strong: Classification and regression with gradient-boosted trees; tuned with stratified CV and calibrated probabilities.
Weak: Statistics
Strong: Built uplift models with proper control groups and variance-aware readouts for leadership.
Python data stack
Libraries ATS often literal-matches.
- pandas
- NumPy
- scikit-learn
- SciPy
- statsmodels
- PySpark
- Jupyter
- virtual environments
- packaging
- profiling
Weak: Python
Strong: pandas/NumPy for feature work; vectorized transforms cutting batch runtime 6x.
Weak: Notebooks
Strong: Moved critical training code from notebooks into versioned packages with tests.
Deep learning & NLP (when relevant)
Use only if credible for your roles.
- PyTorch
- TensorFlow
- transformers
- fine-tuning
- embeddings
- PyTorch Lightning
- CUDA
- mixed precision
- ONNX
- model distillation
Weak: Deep learning experience
Strong: Fine-tuned transformer rankers; evaluated offline/online with guardrail metrics.
Weak: NLP
Strong: Built embedding-based retrieval improving candidate coverage vs. lexical baseline.
SQL, warehouses & experimentation tooling
Analyst/DS hybrid expectations.
- SQL
- Snowflake
- BigQuery
- Redshift
- dbt
- Looker
- Mode
- Amplitude
- Statsig
- Eppo
Weak: SQL skills
Strong: Complex SQL for cohort metrics feeding experiment dashboards.
Weak: Warehouse
Strong: dbt models for trusted experiment metrics with documented grain.
MLOps & production interfaces
Signals you can partner with engineering.
- Docker
- FastAPI
- MLflow
- Airflow
- batch inference
- online inference
- monitoring
- Kubernetes basics
- CI for models
- artifact storage
Weak: Deployed models
Strong: Packaged scoring service in Docker with health checks and structured logs.
Weak: Pipelines
Strong: Airflow DAGs for daily retrain with data quality gates.
Where to use these keywords (ATS + readability)
Skills
Group skills by Modeling / Engineering / Experimentation to mirror JD sections.
Skills
Put the highest-signal libraries first; avoid alphabetical soup.
Experience bullets
Repeat 3–5 must-have skills inside bullets with context (dataset, constraint, outcome).
Summary
Name stack + domain (e.g., experimentation + personalization) in one line.
Experience bullets
If you used PyTorch lightly, say ‘fine-tuned’ vs. ‘research-level architecture design’ honestly.
Common mistakes
- Listing every sklearn model name without evidence you tuned or shipped with them.
- Claiming deep learning depth with only coursework-level projects.
- Burying SQL—many DS roles still filter heavily on SQL proficiency.
- Putting tools you only interacted with in a demo once.