ATS Resume Checker for Data Scientists
Machine Learning appears in 79.0% of 119 recent Data Scientist JDs; Python in 73.1%. See which modeling, experimentation and deployment keywords your resume is missing.
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Based on 119 recent Data Scientist, Machine Learning Scientist, Applied Scientist and Research Scientist job descriptions from Greenhouse, Ashby, Arbeitnow, Jobicy, RemoteOK.
See the keyword data firstWhat Data Scientist job descriptions ask for
Keyword coverage across 119 recent job descriptions. The percentages show how many JDs mention each term — not how many times it appears.
Top 8 by frequency
Full keyword map
All 34 terms that appeared in the sample, grouped by what they do. Percentages are the share of the 119 job descriptions that mention each term.
Programming & data
The working language of experiments and models. Show code you have shipped, not coursework alone.
ML & modeling
The modeling stack. Name the methods and frameworks you can defend in a design review.
Experimentation & analytics
How you prove a model or product change works, not just how you fit it.
Cloud & deployment
Models that never leave a notebook do not create value. Show the serving path.
Business & communication
The highest-leverage differentiator: translating a model into a decision.
Source: Greenhouse, Ashby, Arbeitnow, Jobicy, RemoteOK; collected Oct 26, 2026.
See matched vs missing before you rewrite
After you upload your resume and paste a Data Scientist job description, the checker separates what your resume already proves from what it is missing. This example uses the keywords above.
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Data Scientist · example JD
Matched
Missing
Illustrative output. Your results depend on your resume and the job description.
What this means for your Data Scientist resume
ML, Python and statistics are table stakes
Machine Learning (79.0%), Python (73.1%) and Statistics (67.2%) are the three most common terms. They need to appear in your work, not only in a skills section.
Communication outranks most modeling tools
Communication (65.5%) and Stakeholder Management (51.3%) appear far more often than PyTorch (13.4%) or TensorFlow (9.2%). Show the decision your analysis changed.
Experimentation and causal inference are differentiators
A/B Testing / Experimentation (42.0%), Causal Inference (32.8%) and LLM / Generative AI (30.3%) are more common than MLOps (10.9%) in this sample. Lead with evidence and business impact, then the serving stack.
How to use these keywords without stuffing
A keyword map is a menu, not a checklist to paste into every section. Use the term where it is true, and prove it in the same bullet.
Mirror the JD's exact wording
If the posting says “stakeholder management”, use that phrase instead of a synonym. Include both the acronym and the full form when relevant (A/B testing, experimentation).
Put the keyword inside a result
The skills section gets parsed; the experience bullet gets read. Pair the tool with scale and outcome — users, revenue, latency, hours saved, decision changed.
Cover the cluster, not one word
A JD asking for Machine Learning usually also mentions adjacent tools or methods. Show the cluster in one bullet instead of repeating the same keyword everywhere.
A safe Data Scientist skills-section order
Programming & data → ML & modeling → experimentation → deployment → communication. A model without a metric, decision or production path is a weak resume bullet.
Common Data Scientist resume mistakes
These are the patterns that make a keyword-rich resume read like a tool inventory instead of evidence.
Tool soup
Listing every tool you have touched without saying what you built or changed. A model list without a dataset, metric or business decision reads like coursework. Show the problem, the validation and what changed after the model shipped.
Keyword stuffing
Repeating the same term in the summary, skills list and every bullet. Recruiters notice; it reads like SEO copy, not evidence.
Ignoring the human reader
ATS keywords get you parsed; stakeholder communication and outcomes get you interviewed. Use the keyword as the noun inside a result, not as the whole sentence.
Check your Data Scientist resume against a real JD
Upload a PDF or DOCX, paste the job description, and get the matched and missing keywords.
Before / after: turn duties into evidence
ATS keywords are necessary, but hiring managers still read the bullet. Put the keyword inside a result. These are illustrative rewrites based on the keyword data above.
Built machine learning models.
Built a churn model on 1.2M accounts with XGBoost; lifted retention campaign ROI by 18% against a holdout.
Ran A/B tests.
Designed 12 A/B tests across onboarding and pricing; shipped 4 changes that increased activation by 9%.
Presented insights to stakeholders.
Translated model results into a quarterly roadmap decision for product, marketing and finance leaders.
How we built this page
We collected 119 recent postings titled Data Scientist, Machine Learning Scientist, Applied Scientist and Research Scientist from Greenhouse, Ashby, Arbeitnow, Jobicy, RemoteOK on Oct 26, 2026. Duplicate postings were removed. A keyword counts once per JD; percentages are the share of JDs that mention it.
The sample skews toward product and tech companies. Academic, healthcare and domain-heavy data science roles may weight publications, domain methods and regulatory experience differently.
Method and limitations
- Source mix: Greenhouse 79 · Ashby 14 · RemoteOK 10 · Jobicy 9 · Arbeitnow 7.
- The before/after bullets are illustrative, not real candidate resumes.
- This is an ATS-style keyword and formatting check, not a reproduction of any employer's private screening system.
Data Scientist resume FAQ
What keywords should a Data Scientist resume have?
In our 119-JD sample, Machine Learning (79.0%), Python (73.1%), Statistics (67.2%), Communication (65.5%) and SQL (56.3%) were the most common. The full map groups modeling, experimentation, cloud and business terms. Mirror the job description and keep only what you can defend.
Python or R for a Data Scientist resume?
Python appears in 73.1% of these JDs; R appears in 16.8%. Python is the safer default, but R is still a real differentiator in research, biostatistics and some analytics teams. List the one you have used for real analysis.
Do I need deep learning?
Deep Learning appears in 16.0%, below machine learning (79.0%) and statistics (67.2%). Many data scientist roles are still classical ML, experimentation and SQL-heavy. Add deep learning only when the job or your experience supports it.
How do I show LLM experience?
LLM / Generative AI appears in 30.3%. If you have it, show the task, model, evaluation method and failure modes — not just a list of model names. If you do not, do not stuff the term in.
Do I need MLOps?
Model Deployment / MLOps appears in 10.9% of the sample. It matters more for production-focused roles. If you have shipped a model, show the serving path, monitoring and rollback plan.
How many keywords should a Data Scientist resume have?
Aim for roughly 15–25 role-specific terms across skills and experience. Start with the job description, then keep the terms you can prove with a model, experiment or decision.
Is this a real ATS score?
No. This is an ATS-style keyword and formatting check, not a reproduction of any employer's private screening system. Use it to find gaps, not as a guarantee.
How many job descriptions is this page based on?
119 recent Data Scientist, Machine Learning Scientist, Applied Scientist and Research Scientist job descriptions from Greenhouse, Ashby, Arbeitnow, Jobicy, RemoteOK, collected Oct 26, 2026.
Is the check free?
The first resume scan is free and does not require signup. Paid plans are only for AI rewriting, unlimited scans and exports.
Related role checkers
The same method with role-specific job-description data.
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