Data Science & Machine Learning Resume Keywords: Complete ATS Reference

Skills & Keywords Reference · ATS Weight: critical · Updated 2025-03-15

Data Science and Machine Learning keywords are high-value in ATS screening, but the field is broad enough that vague claims get filtered out. ATS systems in this domain screen for specific algorithms, frameworks, and deployment patterns. Listing 'machine learning' alone is rarely sufficient. This reference guide covers the keyword taxonomy that ATS systems use to evaluate data science resumes.

Primary Keywords

machine learning deep learning Python TensorFlow PyTorch scikit-learn NLP computer vision statistics data analysis A/B testing model deployment

Synonym Groups

ATS systems may recognize these variations. Use the canonical form when possible, but including synonyms ensures broader matching.

machine learning

Also matches: ML, statistical learning, predictive modeling

deep learning

Also matches: DL, neural networks, deep neural networks

NLP

Also matches: natural language processing, text analytics, text mining

computer vision

Also matches: CV, image recognition, object detection

scikit-learn

Also matches: sklearn

SQL pandas NumPy Spark Hadoop data visualization Tableau Power BI R Jupyter

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Top Roles for Data Science & Machine Learning

Role Guide Data Scientist Role Guide Machine Learning Engineer Role Guide Data Analyst Role Guide Research Scientist Role Guide Ai Engineer

Top Industries for Data Science & Machine Learning

Industry Technology Industry Industry Fintech Industry Industry Healthcare Tech Industry

Common Mistakes

Listing 'machine learning' without specifying algorithms or model types used
Not including specific framework names (TensorFlow, PyTorch) alongside general ML claims
Omitting model performance metrics (accuracy, AUC, F1, RMSE) from experience bullets
Listing Jupyter notebooks as a key skill rather than as a tool within a broader workflow
Not distinguishing between exploratory analysis and production ML deployment

Optimal Resume Placement

Technical Skills section with specific frameworks, libraries, and tools listed by name
Experience bullets describing model types, data scale, and business impact metrics
Projects section for portfolio ML work with measurable outcomes
Education section noting relevant coursework in statistics, ML, or AI

Skills Cloud Computing Keywords Skills Python Programming Keywords Skills Data Engineering Keywords