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Data Scientist Resume Example

Data scientist resumes need to balance technical depth with business impact. Show what models you built AND what they achieved for the business. ATS systems look hard for specific tools and frameworks.

PythonTensorFlowPyTorchSQLSparkMLflow

Sample Professional Summary

Data scientist with 5 years in NLP and recommender systems. Built fraud detection model saving $4M annually at FinCo. PhD in Statistics from UCL. Strong in Python, PyTorch, and production ML pipelines.

Use this as a starting point — customise it with your own metrics and experience.

Sample Work Experience Entry

Senior Data ScientistJan 2022 – Present
FinCo
  • Built fraud detection model (XGBoost + neural network) achieving 94.3% precision, saving $4M annually
  • Developed real-time recommendation engine serving 8M users, increasing CTR by 18%
  • Led team of 3 data scientists, establishing MLOps best practices and model monitoring framework

What to Include in a Data Scientist Resume

  • Programming languages: Python, R, SQL (be specific)
  • ML frameworks: TensorFlow, PyTorch, scikit-learn, XGBoost
  • Big data tools: Spark, Databricks, Hadoop
  • Business impact of your models (revenue, cost savings, accuracy improvement)

Expert Tips for Data Scientist Resumes

Tip 1

List every ML/data tool explicitly — ATS scans for Python, TensorFlow, SQL as exact keywords

Tip 2

Quantify model performance: accuracy, precision/recall, revenue impact, cost savings

Tip 3

Include a GitHub link with your portfolio projects

Tip 4

Separate 'Skills' into categories: Languages, ML/AI, Cloud, Databases, Tools

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