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Data Scientist Resume Example

Data scientist resumes should show models in production and the business value they created — not just algorithms.

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Arjun Mehta

Data Scientist
arjun@example.com · +1 555 0142 · Boston, MA · linkedin.com/in/arjun-mehta

Profile

Data scientist with 5 years of experience building machine learning models for pricing, churn and recommendations. Deployed models that generated $4M in incremental revenue. Proficient in Python, SQL, scikit-learn, PyTorch and MLOps.

Experience

Data Scientist

Beacon Commerce · Boston, MA
2021 – Present
  • Built a recommendation model that increased average order value by 9% ($4M per year)
  • Developed churn prediction with 0.86 AUC, enabling targeted retention campaigns
  • Deployed models with Docker and MLflow, reducing time-to-production from weeks to days
  • Communicate findings to executives with clear visual narratives

Data Analyst

Hub Analytics · Cambridge, MA
2019 – 2021
  • Created demand forecasts for 2,000 products
  • Automated data pipelines in Airflow
  • Ran causal-impact analyses for marketing

Education

M.S. Data Science

Northeastern University · Boston, MA
2017 – 2019

Skills

  • Python
  • SQL
  • Machine learning
  • scikit-learn
  • PyTorch
  • Statistics
  • MLOps
  • A/B testing
  • Data visualization
  • Spark

Languages

EnglishNative
SpanishIntermediate

Profile

Data scientist with 5 years of experience building machine learning models for pricing, churn and recommendations. Deployed models that generated $4M in incremental revenue. Proficient in Python, SQL, scikit-learn, PyTorch and MLOps.

Top skills for this role

PythonSQLMachine learningscikit-learnPyTorchStatisticsMLOpsA/B testingData visualizationSpark

Writing tips for this role

  • Lead with business impact of models.
  • Mention deployment and monitoring experience.
  • Keep the tool list relevant to the job ad.

Experience: Data Scientist

  • Built a recommendation model that increased average order value by 9% ($4M per year)
  • Developed churn prediction with 0.86 AUC, enabling targeted retention campaigns
  • Deployed models with Docker and MLflow, reducing time-to-production from weeks to days
  • Communicate findings to executives with clear visual narratives