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Data scientist resume example

A data scientist resume is judged on whether models left the notebook. Recruiters scan for Python, machine learning frameworks and cloud tools; hiring managers look for the metric each model moved and how it reached production.

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The example resume

Wei Chen's data scientist resume, typeset in the Modern template

Typeset by the AI-Resume engine in the Modern template. Every person, company and number in this example is fictional.

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Wei Chen
Data Scientist
wei.chen@example.com · (206) 555-0148 · Seattle, WA · github.com/weichen-ds · linkedin.com/in/weichen
Summary

Data scientist with six years shipping machine learning models to production in Python. Built the demand forecasting system that cut stockouts by 23% across 400 stores, and works closely with engineering to deploy, monitor and retrain models.

Experience
LumenfieldApr 2022 – Present
Senior Data ScientistSeattle, WA
  • Built a demand forecasting model in Python and PyTorch for 400 stores, reducing stockouts by 23% in the first year.
  • Deployed models to production on AWS SageMaker with automated retraining, cutting model refresh time from two weeks to one day.
  • Designed the experimentation framework used for 60 A/B tests a year, standardizing power analysis across three teams.
Copperline InsuranceAug 2019 – Mar 2022
Data ScientistSeattle, WA
  • Developed a machine learning claims triage model in scikit-learn that routed 35% of claims to fast-track, saving $4M a year.
  • Engineered features from 80M rows of claims data in Spark and SQL, improving model AUC from 0.71 to 0.83.
  • Presented model results and limitations to underwriting leadership, securing approval for a company-wide rollout.
Projects
forecastkit2023 – Present
Author · Python, PyTorch
  • Published an open-source library for hierarchical time-series forecasting with 1,100 GitHub stars.
Skills

Modeling: Python, PyTorch, scikit-learn, Machine learning, Statistics

Data: SQL, Spark, AWS, A/B testing

Education
University of Washington2017 – 2019
M.S. Statistics

Why this data scientist resume works

The summary

Data scientist with six years shipping machine learning models to production in Python. Built the demand forecasting system that cut stockouts by 23% across 400 stores, and works closely with engineering to deploy, monitor and retrain models.

It leads with “shipping to production”, the line that separates a data scientist from a notebook, and backs it with one model, its scale and its result.

Bullets, before and after

The same work, described first the way most resumes do it, then the way this one does.

Before

Built forecasting models.

After

Built a demand forecasting model in Python and PyTorch for 400 stores, reducing stockouts by 23% in the first year.

The framework, the scale and the business metric the model moved.

Before

Improved model performance through feature engineering.

After

Engineered features from 80M rows of claims data in Spark and SQL, improving model AUC from 0.71 to 0.83.

Data scale plus a model metric a technical reader can judge.

Before

Worked with leadership on rollout.

After

Presented model results and limitations to underwriting leadership, securing approval for a company-wide rollout.

“And limitations” signals judgment — the quality interviewers probe hardest.

Skills recruiters search for

List the ones you could be interviewed on — and show the most important in a bullet, where they count for about twice as much.

Modeling
  • Python
  • PyTorch
  • TensorFlow
  • scikit-learn
  • XGBoost
Data
  • SQL
  • Spark
  • Snowflake
  • Airflow
Deployment
  • AWS SageMaker
  • Docker
  • MLflow
  • Model monitoring
Methods
  • A/B testing
  • Causal inference
  • Time-series forecasting
  • Statistics

What the scorer would still say

Even a strong resume gets notes. These are the scorer’s own, unedited.

  • Required skills sit in older roles. Machine learning and SQL appear only in roles that ended a while ago. If you have used them since, say so in a recent bullet.

Questions

Should a data scientist resume include Kaggle competitions?

Only if they show something your work history does not, and only with a strong placing. Production impact at work almost always reads as more convincing.

How technical should the bullets be?

Technical enough for the hiring manager — model types, frameworks, metrics like AUC — but every bullet should still end in a result a recruiter can follow.

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