JSON Resume for Data Scientists

Data Scientist Resume in JSON Resume Format

Data science resumes need to balance technical depth (ML models, statistical methods) with business impact (revenue gains, efficiency improvements). JSON Resume's structure supports both.

Data Science Skills

"skills": [
  { "name": "Languages", "level": "Expert", "keywords": ["Python", "R", "SQL"] },
  { "name": "ML Frameworks", "level": "Advanced", "keywords": ["PyTorch", "TensorFlow", "scikit-learn", "XGBoost", "Hugging Face"] },
  { "name": "Data Engineering", "keywords": ["Spark", "Airflow", "dbt", "BigQuery", "Snowflake"] },
  { "name": "Visualization", "keywords": ["Matplotlib", "Seaborn", "Plotly", "Tableau", "D3.js"] },
  { "name": "Methods", "keywords": ["NLP", "Computer Vision", "Time Series", "A/B Testing", "Bayesian Inference"] }
]

Quantifying Data Science Impact

Data science highlights should tie models to business outcomes:

• "Built recommendation engine increasing user engagement by 25% (collaborative filtering, Python)"
• "Developed fraud detection model with 97% precision, saving $2M annually in chargebacks"
• "Designed A/B testing framework used by 5 product teams for feature experimentation"
• "Created NLP pipeline processing 100K support tickets/day, reducing routing time by 60%"

Publications Section

Data scientists often have research publications. Use the publications section:

"publications": [
  {
    "name": "Efficient Transfer Learning for Low-Resource NLP Tasks",
    "publisher": "ACL Workshop on NLP for Low-Resource Languages",
    "releaseDate": "2023-07",
    "url": "https://arxiv.org/abs/xxxx.xxxxx",
    "summary": "Proposed a parameter-efficient fine-tuning method achieving state-of-the-art results with 10x less training data."
  }
]

Education Emphasis

For data science roles, the education section carries more weight than in typical engineering roles. Include relevant coursework, thesis topics, and GPA if strong.