Data Scientist resume skills and keywords
In 1,057 Data Scientist job postings from 601 employers analysed on October 23, 2026, the most requested skills are Python (86 %), Data Science (74 %), SQL (66 %), Machine learning (58 %) and Communication (55 %). Of the top 40 skills, 34 are hard skills, tools or certifications and 6 are soft skills.
Download the full list as a CSV or check your own resume against a posting with the free keyword scanner.
Top 40 skills in Data Scientist job postings
| Skill | Share of postings | Type |
|---|---|---|
| Python | 86 % | Tool |
| Data Science | 74 % | Hard skill |
| SQL | 66 % | Tool |
| Machine learning | 58 % | Hard skill |
| Communication | 55 % | Soft skill |
| Experimentation | 36 % | Hard skill |
| LLMs | 29 % | Tool |
| Mentoring | 29 % | Soft skill |
| Leadership | 29 % | Soft skill |
| Collaboration | 26 % | Soft skill |
| Algorithms | 24 % | Hard skill |
| AWS | 20 % | Tool |
| Decision making | 20 % | Soft skill |
| Problem Solving | 19 % | Soft skill |
| PyTorch | 18 % | Tool |
| Apache Spark | 17 % | Tool |
| Deep learning | 17 % | Hard skill |
| Data Analysis | 17 % | Hard skill |
| R (programming) | 17 % | Tool |
| Data pipelines | 17 % | Hard skill |
| Machine Learning Models | 17 % | Hard skill |
| Data Engineering | 15 % | Hard skill |
| Scikit-learn | 14 % | Tool |
| Pandas | 14 % | Tool |
| Generative AI | 14 % | Tool |
| A/B testing | 13 % | Hard skill |
| GCP | 13 % | Tool |
| TensorFlow | 12 % | Tool |
| APIs | 12 % | Hard skill |
| Databricks | 12 % | Tool |
| AI Tools | 12 % | Tool |
| Statistical Modeling | 12 % | Hard skill |
| Natural language processing | 12 % | Hard skill |
| Feature Engineering | 11 % | Hard skill |
| Statistical analysis | 11 % | Hard skill |
| Artificial Intelligence | 11 % | Hard skill |
| Machine Learning Operations | 11 % | Hard skill |
| Tableau | 11 % | Tool |
| Git | 10 % | Tool |
| Snowflake | 10 % | Tool |
Hard skills vs soft skills vs tools and certifications
Hard skills (17)
- Data Science 74 %
- Machine learning 58 %
- Experimentation 36 %
- Algorithms 24 %
- Deep learning 17 %
- Data Analysis 17 %
- Data pipelines 17 %
- Machine Learning Models 17 %
- Data Engineering 15 %
- A/B testing 13 %
- APIs 12 %
- Statistical Modeling 12 %
- Natural language processing 12 %
- Feature Engineering 11 %
- Statistical analysis 11 %
- Artificial Intelligence 11 %
- Machine Learning Operations 11 %
Soft skills (6)
- Communication 55 %
- Mentoring 29 %
- Leadership 29 %
- Collaboration 26 %
- Decision making 20 %
- Problem Solving 19 %
Tools and software (17)
- Python 86 %
- SQL 66 %
- LLMs 29 %
- AWS 20 %
- PyTorch 18 %
- Apache Spark 17 %
- R (programming) 17 %
- Scikit-learn 14 %
- Pandas 14 %
- Generative AI 14 %
- GCP 13 %
- TensorFlow 12 %
- Databricks 12 %
- AI Tools 12 %
- Tableau 11 %
- Git 10 %
- Snowflake 10 %
Keyword phrases recruiters use
Multi-word phrases that recur across these data scientist postings, with the share of postings that use each one.
How to use this on your resume
Treat the table as a checklist, not a script. Put the skills you genuinely have in your skills section, using the same wording as the posting, and back the top ones with a bullet that shows the result.
Each job posting differs from the average, so compare your resume with the actual ad: paste it into the resume keyword scanner to see which terms you already have and which are missing. The ATS keywords guide explains where to place them so applicant tracking systems read them.
Skip any skill you cannot talk about in an interview; a keyword that falls apart under one question costs more than it gains. For a full layout with these skills in context, see the Data Scientist resume example. Then prepare for the Data Scientist interview questions that test them.
Build a data scientist resume with these skills
Start in the editor, tailor your bullets to the job you want and download an ATS-friendly resume.