jobapplier

Data Science Intern

Coinbase

Skills

Required

  • data analysis
  • programming / modeling
  • statistical modeling
  • SQL
  • Python

Preferred

  • causal inference / machine learning
  • A/B testing
  • creating analyses
  • building models
  • executing experiments
  • maintaining analytical datasets
  • A/B tests

Coinbase as a Data Science Intern for a 12-week summer internship in 2027. As part of the Strategy, Execution, & Analytics team, you'll collaborate with product and engineering teams to turn data into impactful business decisions. You'll gain hands-on experience in a publicly traded crypto-native company, receive mentorship from senior data scientists, and have direct exposure to how data shapes the company.

Responsibilities

  • Responsibilities include creating analyses, building models, executing experiments, maintaining analytical datasets, and delivering data-driven insights.
  • Collaborate with product teams to create analyses and build models that drive measurable business outcomes.
  • Support the design and execution of experiments, including A/B tests and causal inference methods.
  • Build and maintain analytical datasets and pipelines that ensure data integrity and power your analyses and models.
  • Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality

Requirements

  • Ideal candidates are pursuing a degree in a quantitative discipline and have experience in statistical modeling and data analysis.
  • Currently pursuing a BA/BS or graduate degree in a quantitative discipline (e.g., Math, Statistics, Physics, Computer Science, Economics) or equivalent experience
  • Coursework or project experience in statistical modeling, data analysis, or a related quantitative field (e.g., mathematics, economics, physics, computer science)
  • Familiarity with SQL for data analysis and Python for programming or modeling

Preferred

  • Demonstrated interest in experimentation (A/B testing), causal inference, or machine learning through coursework or projects