Resume Keywords for Data Scientists (2026 List)
Updated:
Below are 30+ resume keywords for data scientists, grouped into hard skills, tools and soft skills. Use them to tailor your resume so it passes applicant tracking systems — then check your Data Scientist resume's ATS score free to see which ones you are still missing.
Data science JDs in 2026 frequently mention LLMs, generative AI, RAG and prompt engineering alongside classic ML. Include them only if you have done real work with them, and show it in a project. Also include the evaluation metrics and techniques you use (cross-validation, AUC, precision/recall, SHAP), because senior interviewers and some ATS searches look for them.
Data Scientist hard skills
Core abilities employers screen for. Put the ones you have in your Skills section and prove the top 3–5 in your experience bullets.
- Machine Learning
- Statistics
- Python
- SQL
- Feature Engineering
- Predictive Modeling
- Deep Learning
- Natural Language Processing
- A/B Testing
- Model Deployment
- Data Visualization
- Time Series Analysis
Tools and software
Write tool names exactly as the job post does — many ATS searches for data scientists are tool-specific.
- Scikit-learn
- Pandas
- NumPy
- TensorFlow
- PyTorch
- XGBoost
- Jupyter
- Spark
- MLflow
- AWS
Soft skills
Recruiters look for these too, but they carry more weight when your bullet points demonstrate them.
- Problem Solving
- Communication
- Business Acumen
- Curiosity
- Stakeholder Management
- Critical Thinking
- Storytelling with Data
- Collaboration
Sample skills section for a Data Scientist resume
Copy this structure and keep only the skills you can discuss confidently in an interview:
- Core skills:
- Machine Learning, Statistics, Python, SQL, Feature Engineering, Predictive Modeling, Deep Learning, Natural Language Processing
- Tools:
- Scikit-learn, Pandas, NumPy, TensorFlow, PyTorch, XGBoost, Jupyter, Spark
- Strengths:
- Problem Solving, Communication, Business Acumen, Curiosity
5 Data Scientist resume bullet points with keywords
Each example combines an action verb, a keyword and a measurable result — the format that works for both ATS and recruiters:
- Built an XGBoost churn model (AUC 0.87) that helped the retention team save ₹2.4 crore in annual revenue.
- Deployed a demand forecasting model for 900 SKUs, reducing stock-outs by 22% and excess inventory by 15%.
- Fine-tuned a BERT model to classify 50K customer tickets a month with 91% accuracy, cutting routing time by 70%.
- Designed and analysed 14 A/B tests for pricing and onboarding, informing decisions worth 4% revenue growth.
- Productionised models with MLflow and FastAPI on AWS, reducing model release time from 3 weeks to 3 days.
How to add these keywords without stuffing
- Start with the job description. Highlight every skill and tool it names, then compare with the lists above.
- Add matching skills to your Skills section, grouped like the sample above, using the employer's exact spelling.
- Use the 3–5 most important keywords — for data scientists usually Machine Learning, Statistics, Python — inside experience or project bullets with a result.
- Mention your primary skill once in your summary so it appears near the top of the page.
- Never list skills you don't have. Interviewers will test them, and keyword stuffing can be flagged by recruiters.