ATS Keywords for Data Scientists (2026) — Junior, Mid, Senior, Staff

Data science JDs in 2026 split sharply by track — analyst-track (SQL + experimentation), ML-engineering-track (production models, MLOps), and research-track (novel methods, publications). These keywords are organized so you can match the role's actual focus. Run your resume through our free scanner to see which parse cleanly from your file.

Last updated: 2026-05-15

66 keywords across 8 categories

30 JDs sampled + 2 O*NET occupations

How we sourced these →

Listing the right keywords doesn't matter if your ATS can't extract them. Run your resume through our free scanner — see which of these keywords actually parse from your file.

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Always include (every level)

These keywords appear in roughly 90%+ of the job descriptions we sampled across all seniority levels. If they're missing from your resume — junior or senior — you're failing the keyword match before any review happens.

Foundations every data-science resume needs

These appear in nearly every data-science JD across tracks and seniority. Missing them is a structural red flag.

Python
O*NET + JD
SQL
O*NET + JD
Statistics
O*NET + JD
Data visualization
O*NET + JD
A/B testing
O*NET + JD
Machine learning
O*NET + JD
Jupyter
JD
Git
O*NET + JD

Junior / Entry-level keywords (0–3 years)

Junior job descriptions filter heavily on specific technical training. Your resume needs explicit, named tokens — not generic skill categories.

Junior Data Scientist / Analyst vocabulary

Junior DS JDs filter on toolkit literacy and experimentation basics. Specific library names beat 'Python experience'.

pandas
JD
NumPy
JD
scikit-learn
JD
matplotlib
JD
seaborn
JD
Exploratory data analysis (EDA)
JD
Hypothesis testing
O*NET + JD
Regression analysis
O*NET + JD
Tableau
JD
Looker
JD

Data manipulation & basic ML (junior signal)

Junior DS roles check for working knowledge of core ML algorithms by name. Generic 'ML knowledge' won't match.

Linear regression
O*NET + JD
Logistic regression
O*NET + JD
Decision trees
O*NET + JD
Random forest
JD
Gradient boosting (XGBoost / LightGBM)
JD
Cross-validation
JD
Feature engineering
JD

Mid-level keywords (3–6 years)

Mid-level JDs add architecture vocabulary and ownership signals. The shift from junior is that you're expected to own features end-to-end and design components, not just implement them.

Mid-level ML & experimentation vocabulary

Mid-level DS JDs filter on production-aware ML and rigorous experimentation. Naming specific frameworks signals depth.

TensorFlow
O*NET + JD
PyTorch
O*NET + JD
Keras
JD
Hyperparameter tuning
JD
Model evaluation
O*NET + JD
Bias-variance tradeoff
JD
Experimentation platforms
JD
Causal inference
JD
Bayesian methods
JD
Time-series forecasting
O*NET + JD

MLOps & infrastructure (mid signal)

Mid-level DS / MLE JDs increasingly require productionization vocabulary. List these where they apply truthfully.

MLOps
JD
Docker
JD
AWS SageMaker
JD
MLflow
JD
Kubeflow
JD
Model deployment
JD
Model monitoring
JD
Feature stores
JD
Airflow
JD
dbt
JD

Senior keywords (6–10+ years)

Senior JDs filter on system-design depth and technical leadership. Even individual-contributor senior roles expect cross-team influence vocabulary.

Senior Data Scientist / ML Engineer vocabulary

Senior JDs filter on deep learning, system-level ML design, and cross-functional collaboration with product and engineering.

Deep learning
O*NET + JD
Transformers
JD
Large language models (LLMs)
JD
Recommendation systems
JD
Computer vision
O*NET + JD
Natural language processing (NLP)
O*NET + JD
Model architecture design
JD
Distributed training
JD
A/B testing platform design
JD

Leadership signals (senior data scientist)

Senior IC and management-track DS JDs both filter on cross-team influence and mentorship vocabulary.

Technical mentorship
JD
Cross-functional partnership
JD
Stakeholder communication
JD
Research roadmap
JD
Data strategy
JD

Staff / Principal / Lead keywords (10+ years)

These roles filter for strategy, influence-over-authority, and org-wide impact. Senior keywords alone won't pass these filters.

Staff / Principal / Director DS vocabulary

Top-tier DS JDs filter on research direction, organizational impact, and publication / external visibility.

Research direction
JD
Org-wide ML strategy
JD
Hiring and team building (DS)
JD
Technical strategy
JD
Publications / patents
JD
External conferences (NeurIPS, ICML)
JD
Multi-team coordination
JD

How to actually use these

How to actually use these in your data science resume:

Specify your track. Are you analyst-track (experimentation, causal inference, business metrics) or ML-engineering-track (production models, MLOps) or research-track (novel methods, publications)? Pick one as your headline and weight your keywords toward it. A "machine learning engineer" who lists 0 production tools (Docker, SageMaker, MLflow) reads as confused to recruiters AND fails the ATS filter for MLE roles.

Show production impact, not Kaggle scores. "Built XGBoost model achieving 0.87 AUC" is weak — it's table-stakes. "Built XGBoost-based churn model deployed to production via SageMaker; reduced 30-day churn by 14%; serves 2M users/day with <100ms p95 latency" hits 6+ keyword clusters AND demonstrates real-world ML.

List frameworks you've actually shipped against. "Python, TensorFlow, PyTorch, Keras, JAX, MXNet, Theano, scikit-learn" is keyword stuffing and recruiters know it. Pick the 4-5 you've used in production roles and drop the rest.

The "deep learning" trap. Every DS resume claims it. The differentiator is the artifact: list the specific model architecture you built or fine-tuned (BERT, T5, LLaMA-2 fine-tune, custom CNN for image classification, etc.). Generic "deep learning" = no signal; specific architecture = high signal.

Run the scanner. Data science resumes are template-heavy — many candidates use Overleaf LaTeX templates with custom column layouts that look beautiful but parse poorly into Workday and Greenhouse. Upload your file to see what extracts. If you've used a sidebar for skills, that's likely getting scrambled.

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