Data Scientist resume

How to write a data scientist resume that passes the ATS

Updated 7 October 2026

Data science posts list Python, machine learning, statistics and increasingly MLOps and LLMs. The gap between a rejected and a shortlisted resume is usually specificity: which models, on how much data, with what measured lift.

What the ATS and recruiters look for in a data scientist

Python and its ML stack (scikit-learn, PyTorch/TensorFlow, pandas), SQL, statistics and experimentation, and the deployment side (MLflow, Docker, cloud ML services). Managers want to see models in production and the metric they moved, not Kaggle scores.

Data Scientist resume keywords

Use the exact spelling the job post uses. Put the post's must-haves first in your skills section and repeat the important ones inside bullets with results.

Hard skills

Machine learning (supervised, unsupervised)
Statistical modelling
Experiment design and A/B testing
Feature engineering
Deep learning
NLP / LLMs
Time-series forecasting
Model deployment and monitoring
Data pipelines
Causal inference
Model evaluation
Data storytelling

Tools & platforms

Python
scikit-learn
PyTorch
TensorFlow
pandas / NumPy
SQL
Spark
MLflow
Airflow
AWS SageMaker / Vertex AI
Docker
Tableau

Soft skills (show in bullets)

Translating business problems into models
Communication with non-technical stakeholders
Rigor
Collaboration with engineering
Prioritisation

How to structure each section

Summary. Domain, model types and one production result: "Data scientist; demand forecasting and recommender systems in production for a 2M-user marketplace."
Skills. ML & Statistics, Languages, Data & Pipelines, Deployment. Match the post's framework (PyTorch vs TensorFlow).
Experience. Problem, data size, method, metric before/after, and whether it shipped.
Publications / Projects. Keep to what's relevant to the role. One line each, with a link.

Before and after: data scientist bullet points

BeforeDeveloped machine learning models for the recommendation system.

AfterBuilt and deployed a two-tower recommender (PyTorch, SageMaker) over 50M interactions, raising click-through 18% in an A/B test and serving 2M users daily.

Why it works: Model type, framework, platform, data size, experiment result, scale.

BeforePerformed data analysis and created predictive models.

AfterForecast weekly demand for 3,000 SKUs with gradient boosting (LightGBM) and hierarchical reconciliation, cutting stock-outs 22% and excess inventory $1.4M.

Why it works: Specific method and two business metrics.

Mistakes that get data scientist resumes filtered out

Listing every algorithm you've heard of; list the ones you've shipped.
Kaggle rankings as the headline for a role that wants production experience.
No mention of SQL; almost every post asks for it.
A three-page academic CV when the post wants a two-page resume.

Tailor it to the job in minutes

Resumeni reads the job description, finds the keywords you're missing, and rewrites your resume for the role while keeping your own layout and fonts. The first check-up is free: you see your ATS score, the gaps and a few rewritten lines before paying anything. Or run the free ATS checker on your current resume first.

Frequently asked questions

Should I include my PhD research?

Yes, framed as projects: problem, method, result. Trim publication lists to the relevant few.

How do I show MLOps experience?

Name the tools (MLflow, Airflow, Docker, a cloud ML platform) inside bullets about models you deployed and monitored.

LLM experience: where does it go?

In skills ("LLMs, RAG, prompt engineering, fine-tuning") and in a bullet with a measured outcome, the same as any model.