Data scientist resume template: example & tips
Data science teams look for people who turn data into decisions – not just models. On your resume, hiring managers want to see which methods and tools you use, which problems you solved and what your work changed for the business. A model with 99% accuracy that nobody uses is worth less than a simple one that improved a decision.
Our pick
Mono Tech
PremiumATS-safe ✅Why this template
Projects and tools come first; the clear structure and monospace skills let the numbers in your bullets stand out. Single-column and cleanly readable in applicant tracking systems.
Example summary
Your summary sits right below your name. Replace everything in square brackets with your own details.
Data scientist with [number] years of experience in [industry], focused on [e.g. forecasting, NLP, experimentation]. Skilled in Python, SQL and [frameworks]; most recently [project with result, e.g. a demand forecast that cut overstock by [number]%]. Experienced in taking models to production and explaining results to business teams.
What a strong Data scientist resume looks like
Sample bullet points for your experience. Where it says [number], put your own figure – made-up numbers fall apart in the interview.
- Built a demand forecast for [number] products that reduced overstock by [number]%
- Developed a churn model the customer team used to reach [number] at-risk accounts
- Designed and analyzed [number] A/B tests per quarter with the product team
- Took [number] models to production with monitoring and automated retraining
- Automated a manual report in Python, saving [number] hours per month
- Classified customer requests with NLP at [number]% accuracy on a held-out test set
- Ran data literacy workshops for [number] business colleagues
Hard skills
- Python (pandas, NumPy, scikit-learn)
- SQL and data modeling
- Statistics and experiment design (A/B tests, causal inference)
- Machine learning (classification, regression, clustering, time series)
- Deep learning (e.g. PyTorch) and NLP
- Data visualization (e.g. Tableau, Power BI, Plotly)
- MLOps (e.g. MLflow, Docker, cloud deployment)
- Big data tools (e.g. Spark, Databricks)
Soft skills – with proof
- Explaining results to business teams without jargon
- Finding the question behind the question
- Pragmatism: the simplest model that solves the problem
- Care with data quality and assumptions
ATS keywords for Data scientist resumes
Terms that come up often in job ads for this role. Only use what is true for you – ideally in the wording of the ad you are applying to.
- Data Scientist
- Data Science
- Machine Learning
- Python
- SQL
- Statistics
- A/B Testing
- scikit-learn
- PyTorch
- NLP
- Forecasting
- MLOps
- Spark
- Data Visualization
Tips for your Data scientist resume
- Describe the business effect of your models, not just the metric: which decision got better, which costs dropped?
- Report metrics honestly and with context – accuracy on which data, improvement over which baseline.
- A GitHub profile or portfolio with one or two well-documented projects beats many half-finished notebooks.
- Separate analysis, modeling and engineering work. Roles differ widely in how much production code they expect.
- Publications, conference talks or competitions (e.g. Kaggle) belong briefly in their own section if relevant.
Salary
Pay depends on location, employer, experience and certification – a single number says little. For current figures by occupation, see the Occupational Outlook Handbook (U.S. Bureau of Labor Statistics).
Occupational Outlook Handbook (U.S. Bureau of Labor Statistics)