Data Science ATS Keyword Strategy
Data scientist is one of the most technically diverse job titles in tech — the same title can mean a pure ML researcher at one company and a business analyst with Python skills at another. This means your keyword strategy must be tailored to each specific job description rather than using one static resume. The terms below cover the full landscape; pick the ones that appear in the job posting you are targeting.
Core Data Science Keywords
- Machine learning, deep learning, supervised learning, unsupervised learning
- Python, R, SQL, Scala
- Statistical modeling, regression, classification, clustering
- Data pipeline, ETL, data wrangling, feature engineering
- A/B testing, hypothesis testing, statistical significance
- Data visualization, dashboards, reporting
- Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
Machine Learning and AI Keywords
- Neural networks, convolutional neural network (CNN), recurrent neural network (RNN)
- Natural language processing (NLP), large language models (LLM)
- TensorFlow, PyTorch, Keras, XGBoost, LightGBM
- Model training, model evaluation, cross-validation, hyperparameter tuning
- Reinforcement learning, transfer learning, fine-tuning
- MLOps, model deployment, model monitoring
- Hugging Face, transformers, BERT, GPT
Data Infrastructure and Engineering Keywords
- Spark, Hadoop, Databricks, Airflow
- AWS, GCP, Azure, cloud computing
- BigQuery, Redshift, Snowflake, dbt
- Data warehouse, data lake, data mesh
- Kafka, streaming data, real-time analytics
- Docker, Kubernetes, CI/CD for ML
Analytics and Business Keywords
- Business intelligence, KPIs, OKRs, north star metric
- Tableau, Power BI, Looker, Metabase
- Cohort analysis, funnel analysis, retention analysis
- Forecasting, time series analysis, ARIMA
- Experimentation platform, causal inference
- Stakeholder communication, data storytelling
How to Tailor Keywords by Job Type
Research-focused roles (ML Engineer, Research Scientist) weight PyTorch, model architecture, and publications. Product-facing roles (Product Data Scientist) weight experimentation, A/B testing, and business metrics. Analytics-heavy roles weight SQL, dashboards, and stakeholder communication. Scan the job description for which cluster of terms appears most frequently — that tells you exactly which direction to skew your resume.
Check Your Data Science Resume Now
Paste your resume into the free ATS checker at airesume.pro/ats-checker along with the job description you are targeting. It shows your exact keyword match percentage and lists the specific terms you need to add before applying.
Ready to check your ATS score?
Check My Resume Free →