Data Science Intern

Robinhood helps users invest in stocks, ETFs, options, and cryptocurrencies through commission-free trading with no minimum account requirements.

Series D0 current maintainers0 active leadsTeam intelligence

Maintainer signals as of 9/25/2026

United States
About Robinhood

Robinhood helps retail investors access financial markets through commission-free trading of stocks, ETFs, options, and cryptocurrencies. With it, users can invest with no account minimums, earn rewards through retirement accounts with matching contributions, and access advanced trading tools. Robinhood democratizes investing by making financial markets accessible to everyone, not just wealthy investors.

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Skills

Candidate Availability

Required and preferred rules are kept separate and reflect the wording in the original posting.

About the Role

You will analyze product performance and identify growth opportunities. You will design and evaluate experiments, develop predictive and causal models, build dashboards and visualizations, communicate recommendations, and partner with product, engineering, operations, and other functions on analytics projects.

Requirements

  • Enrollment in a full-time graduate degree program with expected graduation in Winter 2027 or Spring 2028
  • Graduate study in mathematics, statistics, engineering, natural science, or another quantitative field
  • Data-science, statistical-analysis, and machine-learning expertise
  • Python and SQL programming skills
  • Experience with data-visualization tools and techniques
  • Financial-technology experience preferred

Responsibilities

  • Analyze product data to identify growth opportunities and improve business metrics
  • Design, implement, and analyze A/B experiments and causal-inference analyses
  • Develop predictive modeling, uplift modeling, causal inference, and experimentation-design expertise
  • Define and maintain product-performance metrics and scalable dashboards
  • Build data visualizations and narratives to communicate insights and recommendations
  • Lead large-scale analytics projects to uncover user insights and inform product strategy
  • Integrate data-driven decision-making into product and business processes
  • Promote established methods and a data-informed culture