Data Science Intern
- New York, United States
- Internship
- Intern/New Grad
- Bachelor's degree
- Posted
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
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