Engineering & IT· Free keyword tool
Data Scientist Resume Keywords
These data scientist resume keywords cover the statistical methods, machine learning frameworks, and ATS terms hiring systems scan for — the skills that prove you can turn raw data into business decisions and get a data science resume past the filter.
59 keywords across 6 categories — toggle, select, and copy directly into your resume.
Hard Skills(12)Role-specific technical abilities
Tools & Tech(13)Software and platforms you operate
Soft Skills(7)How you work with people
Certifications(5)Credentials recruiters scan for
Action Verbs(12)Strong openers for bullet points
ATS Key Terms(10)Phrases parsers weight most
Want these woven into a real resume?
ATSFixer drops the right keywords into the right sections of your resume, then scores it against any job description so you know exactly what's still missing.
Build My Resume Free →What Data Scientist ATS keywords matter most?
Applicant tracking systems parsing Data Scientistresumes weigh hard skills and tool proficiency most heavily — exact terms, not synonyms. The terms below appear most frequently in Data Scientist job postings and carry the most weight with automated screening algorithms.
Top hard skills
- Machine Learning
- Statistical Modeling
- Data Wrangling
- Feature Engineering
- Exploratory Data Analysis
- Natural Language Processing (NLP)
- Deep Learning
- Regression Analysis
Top tools & technologies
- Python
- R
- SQL
- TensorFlow
- PyTorch
- scikit-learn
- Pandas
- NumPy
Key certifications ATS scans for: AWS Certified Machine Learning – Specialty, Google Professional Data Engineer, Coursera Deep Learning Specialization.
Data Scientistresume keywords — frequently asked questions
- What keywords should a data scientist put on a resume?
- Lead with your modeling methods (machine learning, regression, NLP), your primary language (Python or R), and the frameworks the posting names (TensorFlow, PyTorch, scikit-learn). Add deployment terms if you have MLOps experience.
- Is Python the most important keyword for data scientists?
- Yes — Python is the single most searched skill for data science roles. SQL comes second. List both prominently, then add your ML libraries.
- Should I list both TensorFlow and PyTorch?
- List both if you are proficient in both — it broadens your match. Lead with the one the posting names. Research and academia roles lean PyTorch; production ML at large companies varies.
- How do I show machine learning impact on a resume?
- Use accuracy or business metrics: "Built a churn prediction model achieving 87% accuracy, saving an estimated $2.4M annually" or "Reduced model inference latency 60% with quantization."
- Do data scientists need cloud certifications?
- AWS Machine Learning Specialty and GCP Professional Data Engineer are strong ATS keywords for roles that mention cloud ML. For pure research roles, publications and GitHub projects matter more.
- How many keywords should a data science resume include?
- Use 14–20 across methods, tools, and ML frameworks, prioritizing the techniques and stack the posting names.
Related Engineering & IT roles
From the blog
Keywords sourced from O*NET occupational data and current job postings. No keywords are invented.
Next step
See how many of these your resume already has.
Upload your resume, paste a job description, and get an instant ATS score with every keyword you're missing.