Data Scientist Resume Keywords (2025): 60+ ATS Skills to Land Interviews
ResumeAdapter TeamShare this post
Send this to a friend who’s also job searching.
🚨 Not getting data scientist interviews? Your resume is missing critical ML/AI keywords.
In 2025, over 97% of tech companies use ATS to filter data scientist resumes. Missing terms like "Machine Learning," "TensorFlow," or "Deep Learning" can instantly disqualify you—even with years of data analysis experience.
This guide gives you 60+ ATS-approved data scientist keywords, organized by category, with real examples and optimization strategies.
👉 Scan Your Data Scientist Resume for Missing Keywords - Free
Why Data Scientist Resume Keywords Matter in 2025
The brutal truth: Data science roles are highly technical and keyword-dependent, requiring a unique blend of ML/AI, statistics, and engineering keywords.
Recruiters and ATS systems scan your resume for:
- ✅ ML frameworks (TensorFlow, PyTorch, Scikit-learn, XGBoost, LightGBM)
- ✅ Programming languages (Python, R, SQL, Scala, Java)
- ✅ ML methodologies (Machine Learning, Deep Learning, NLP, Computer Vision, Reinforcement Learning)
- ✅ Data science tools (Jupyter, Pandas, NumPy, Matplotlib, Seaborn)
- ✅ MLOps & deployment (Model Deployment, MLOps, Docker, Kubernetes, Model Serving)
- ✅ Cloud platforms (AWS SageMaker, Azure ML, GCP AI Platform, Databricks)
If your resume doesn't match the job's ML/AI vocabulary, it gets filtered out—often before a human ever sees it.
According to LinkedIn's 2024 Global Talent Trends report, over 90% of tech companies rely on ATS systems to filter data scientist resumes. The Kaggle State of Data Science 2024 survey confirms that Python, SQL, and machine learning frameworks are the primary screening criteria for most organizations.
The Data Scientist Keyword Gap Problem
75% of data scientist resumes are rejected by ATS before reaching a recruiter.
The #1 reason? Missing ML frameworks, Python libraries, and MLOps keywords.
Example: A data scientist resume missing "TensorFlow" or "Deep Learning" gets filtered out, even if the candidate has 5 years of data analysis experience.
The solution: Use this comprehensive keyword guide to ensure your resume includes every term data science recruiters search for. Reference industry standards from TensorFlow and PyTorch documentation to ensure you're using the correct terminology.
60+ Essential Data Scientist Resume Keywords (2025)
Our research across hundreds of data scientist job listings shows that successful resumes must include a blend of:
🤖 Machine Learning Frameworks & Libraries
| Framework | Related Keywords |
|---|---|
| TensorFlow | TensorFlow, Keras, TensorFlow Lite, TensorFlow Serving, TensorBoard, TensorFlow Extended (TFX) |
| PyTorch | PyTorch, Torch, PyTorch Lightning, Torchvision, Torchaudio, ONNX |
| Scikit-learn | Scikit-learn, sklearn, Machine Learning Algorithms, Model Training, Model Evaluation |
| XGBoost | XGBoost, Gradient Boosting, LightGBM, CatBoost, Ensemble Methods |
| MLlib | Apache Spark MLlib, Distributed Machine Learning, Spark ML |
🐍 Programming Languages & Data Science Tools
| Category | Keywords |
|---|---|
| Programming Languages | Python, R, SQL, Scala, Java, C++, Julia |
| Data Manipulation | Pandas, NumPy, Polars, DataFrames, Data Cleaning, Data Preprocessing |
| Data Visualization | Matplotlib, Seaborn, Plotly, Bokeh, D3.js, Data Visualization |
| Development Environment | Jupyter Notebook, JupyterLab, Google Colab, VS Code, PyCharm |
| Statistical Analysis | Statistical Analysis, Hypothesis Testing, A/B Testing, Statistical Modeling |
🧠 Machine Learning Methodologies
| Category | Keywords |
|---|---|
| Supervised Learning | Supervised Learning, Classification, Regression, Decision Trees, Random Forest, SVM |
| Unsupervised Learning | Unsupervised Learning, Clustering, K-Means, DBSCAN, Dimensionality Reduction, PCA |
| Deep Learning | Deep Learning, Neural Networks, CNN (Convolutional Neural Networks), RNN, LSTM, GRU, Transformers |
| Natural Language Processing | NLP, Natural Language Processing, Text Mining, Sentiment Analysis, Named Entity Recognition, BERT, GPT |
| Computer Vision | Computer Vision, Image Classification, Object Detection, Image Segmentation, OpenCV, YOLO |
| Reinforcement Learning | Reinforcement Learning, Q-Learning, Deep Q-Networks (DQN), Policy Gradients |
📊 Data Science & Analytics
| Category | Keywords |
|---|---|
| Data Science | Data Science, Data Mining, Exploratory Data Analysis (EDA), Feature Engineering, Feature Selection |
| Big Data | Big Data, Apache Spark, Hadoop, Hive, HBase, Distributed Computing |
| Time Series | Time Series Analysis, Forecasting, ARIMA, Prophet, LSTM for Time Series |
| Recommendation Systems | Recommendation Systems, Collaborative Filtering, Content-Based Filtering, Matrix Factorization |
| Anomaly Detection | Anomaly Detection, Outlier Detection, Fraud Detection, Isolation Forest |
🚀 MLOps & Model Deployment
| Category | Keywords |
|---|---|
| MLOps | MLOps, Machine Learning Operations, Model Deployment, Model Serving, Model Versioning |
| Model Deployment | Model Deployment, Model Serving, REST APIs, Flask, FastAPI, Docker, Kubernetes |
| CI/CD for ML | CI/CD for ML, Model Testing, Model Validation, Automated ML Pipelines |
| Model Monitoring | Model Monitoring, Model Drift, Model Performance Tracking, A/B Testing for ML |
| Model Registry | Model Registry, MLflow, Weights & Biases (W&B), Experiment Tracking |
☁️ Cloud Platforms & ML Services
| Platform | Related Keywords |
|---|---|
| AWS | AWS SageMaker, AWS Lambda, AWS Glue, S3, EC2, EMR, AWS Bedrock |
| Azure | Azure Machine Learning, Azure Databricks, Azure ML Studio, Azure Cognitive Services |
| GCP | Google Cloud AI Platform, Vertex AI, BigQuery ML, TensorFlow Extended (TFX) |
| Databricks | Databricks, Spark ML, MLflow, Delta Lake, Databricks Runtime for ML |
| Snowflake | Snowflake, Snowpark, ML in Snowflake, Data Warehousing |
🗄️ Databases & Data Storage
| Category | Keywords |
|---|---|
| SQL Databases | SQL, PostgreSQL, MySQL, SQL Server, SQLite, Database Design |
| NoSQL Databases | MongoDB, Cassandra, Redis, DynamoDB, Document Databases |
| Data Warehouses | Data Warehousing, Snowflake, BigQuery, Redshift, Data Lake, Delta Lake |
| Data Pipelines | ETL, ELT, Data Pipelines, Apache Airflow, Prefect, Data Orchestration |
📈 Model Evaluation & Metrics
| Category | Keywords |
|---|---|
| Model Evaluation | Model Evaluation, Cross-Validation, Train-Test Split, Hyperparameter Tuning, Grid Search, Random Search |
| Performance Metrics | Accuracy, Precision, Recall, F1-Score, ROC-AUC, RMSE, MAE, R², Confusion Matrix |
| Model Optimization | Model Optimization, Feature Engineering, Hyperparameter Optimization, AutoML, Model Selection |
How to Integrate Keywords into Your Resume
✅ Strong Example: Keyword-Optimized Data Scientist Resume
Experience Section:
Senior Data Scientist | Tech Company | 2021 - Present
- Developed machine learning models using Python, TensorFlow, and PyTorch, improving prediction accuracy by 25% and reducing false positives by 40%
- Built deep learning models for computer vision tasks using CNN architectures, achieving 95% accuracy in image classification
- Implemented NLP solutions using BERT and transformers for sentiment analysis, processing 1M+ customer reviews with 90% accuracy
- Designed and deployed MLOps pipelines using Docker, Kubernetes, and MLflow, reducing model deployment time from weeks to hours
- Created feature engineering pipelines and data preprocessing workflows using Pandas and NumPy, improving model performance by 15%
- Developed recommendation systems using collaborative filtering and matrix factorization, increasing user engagement by 30%
- Built time series forecasting models using ARIMA and Prophet for demand prediction, reducing inventory costs by 20%
- Collaborated with engineering teams to deploy models to production using AWS SageMaker and REST APIs, serving 10M+ predictions daily
Skills Section:
ML Frameworks: TensorFlow, PyTorch, Scikit-learn, XGBoost, LightGBM, Keras
Programming: Python, R, SQL, Scala, Java
Data Science Tools: Pandas, NumPy, Matplotlib, Seaborn, Plotly, Jupyter Notebook
ML Methodologies: Machine Learning, Deep Learning, NLP, Computer Vision, Reinforcement Learning, Time Series Analysis
MLOps & Deployment: MLOps, Model Deployment, Docker, Kubernetes, MLflow, CI/CD for ML, Model Monitoring
Cloud Platforms: AWS SageMaker, Azure ML, GCP Vertex AI, Databricks, Snowflake
Databases: SQL, PostgreSQL, MongoDB, Redis, BigQuery, Data Warehousing
Statistics: Statistical Analysis, Hypothesis Testing, A/B Testing, Statistical Modeling
❌ Weak Example: Missing Keywords
Experience Section:
Data Analyst | Tech Company | 2021 - Present
- Worked with data to build predictive models
- Used Python for analysis and visualization
- Helped improve business decisions with data insights
- Created reports and dashboards
Skills Section:
Python, Data Analysis, Statistics, Excel
Why it fails:
- ❌ No ML frameworks mentioned (TensorFlow, PyTorch, Scikit-learn)
- ❌ Missing ML methodologies (Deep Learning, NLP, Computer Vision)
- ❌ No MLOps or model deployment keywords
- ❌ Vague descriptions that don't match ATS keyword searches
- ❌ No quantifiable results or model performance metrics
Keyword Integration Strategy
1. Match the Job Description
Read the job posting carefully and identify:
- Required ML frameworks (TensorFlow, PyTorch, Scikit-learn)
- Preferred programming languages (Python, R, SQL)
- ML methodologies (Deep Learning, NLP, Computer Vision)
- MLOps and deployment requirements (Docker, Kubernetes, AWS SageMaker)
2. Use Keywords Naturally
Don't keyword stuff. Integrate keywords into:
- Summary/Objective: Mention your primary ML expertise (e.g., "Data Scientist with expertise in Machine Learning, Deep Learning, and MLOps using Python, TensorFlow, and PyTorch")
- Experience Bullets: Include frameworks, methodologies, and tools with context and measurable results
- Skills Section: List all relevant ML frameworks, programming languages, and tools, organized by category
- Projects Section: Mention technologies used in ML projects, research, or open-source contributions
💡 Data Analyst vs Data Scientist? If you work with data analysis, check our Data Analyst Resume Keywords guide to ensure you include both analytics and ML keywords.
3. Include Both General and Specific Terms
- General: Machine Learning, Data Science, Deep Learning, NLP, Computer Vision
- Specific: TensorFlow, PyTorch, BERT, CNN, AWS SageMaker, MLflow
4. Show Impact with Keywords
Instead of: "Built ML models to improve predictions"
Write: "Developed machine learning models using Python and TensorFlow, improving prediction accuracy by 25% and reducing false positives by 40%"
5. Highlight Modern ML Practices
Include keywords that show you're up-to-date:
- MLOps: Model Deployment, MLOps, Docker, Kubernetes, MLflow
- Modern Frameworks: TensorFlow, PyTorch, Transformers, BERT
- Cloud ML: AWS SageMaker, Azure ML, GCP Vertex AI, Databricks
Related Articles
Internal Guides
- Complete Resume Keywords List Hub - Browse all role-specific keyword guides
- Data Analyst Resume Keywords (2025) - Data analysis and SQL keywords (related to data science)
- Software Engineer Resume Keywords (2025) - Full stack development and system design keywords
- DevOps Engineer Resume Keywords (2025) - MLOps and infrastructure automation keywords
- Backend Developer Resume Keywords (2025) - API development and system architecture keywords
- How to Pass ATS in 2025 - Complete ATS compatibility guide
- Why ATS Rejects Qualified Resumes - Common rejection reasons and fixes
- Free ATS Resume Scanner - Test your resume compatibility instantly
Data Science Resources
- LinkedIn Data Scientist Jobs - Find data scientist roles and analyze job descriptions
- Indeed Data Scientist Career Guide - Data scientist resume tips and examples
- Glassdoor Data Scientist Insights - Data scientist resume best practices and salary data
- Kaggle - Data science competitions, datasets, and learning resources
- TensorFlow Documentation - Official TensorFlow framework documentation
- PyTorch Documentation - Official PyTorch framework documentation
- Scikit-learn Documentation - Official Scikit-learn library documentation
Alternative Tools
- ResumeWorded - Resume review tool with data science focus
- Jobscan - Resume-to-job matching for data scientist roles
- Wozber - ATS-friendly resume builder with keyword optimization
Ready to Optimize Your Data Scientist Resume?
Don't guess which keywords you're missing.
Test your resume now and get instant feedback.
👉 Scan Your Data Scientist Resume for Missing Keywords - Free