About the team + role

We are building an elite team, applying frontier technologies to the world's biggest financial problems. We're looking for bold thinkers. Sharp problem-solvers. Builders who are wired to make an impact. Robinhood isn't a place for complacency, it's where ambitious people do the best work of their careers. We're a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards.

Brokerage products are the bread and butter of Robinhood. The Brokerage team is fast-paced, dynamic, and deeply collaborative, driving innovation across our suite of brokerage products.

As a Data Scientist Intern on the Brokerage Data Science team, you'll be part of a high-impact, multi-functional group working closely with partners across product, engineering, user research, design, marketing, finance, and operations. Together, you’ll unlock the power of data to shape the future of our brokerage business and deliver outstanding value to our customers.

We’re looking for a dedicated and ambitious graduate student to help us accelerate product growth through world-class experimentation, analysis, and data-informed strategy. Your work will directly influence the roadmap, uncover new opportunities, and improve the trading experience for millions of users.

This role is based in our Menlo Park office(s), with in-person attendance expected at least 3 days per week.

At Robinhood, we believe in the power of in-person work to accelerate progress, spark innovation, and strengthen community. Our office experience is intentional, energizing, and designed to fully support high-performing teams.

What you’ll do

• Partner directly with a current team member on a specific product line (e.g., Agentic Trading, Prediction Markets, Portfolio Margin, Trading Products, Trading Tools, Robinhood Social, Legend, etc).
• Perform deep-dive analyses to identify product growth opportunities and drive improvements in core business metrics.
• Design, plan, implement, and analyze A/B experiments, and conduct causal inference analysis to evaluate product changes.
• Gain expertise in predictive modeling, uplift modeling, causal inference, and experimentation design.
• Define and maintain key product performance metrics; build scalable dashboards to monitor these metrics and advise decision-making.
• Build compelling data visualizations and narratives to effectively communicate insights and recommendations to multi-functional partners, collaborators, and senior leadership.
• Lead and implement large-scale analytics projects to uncover user insights, inform product strategy, and deliver measurable business impact.
• Partner closely with Product, Engineering, Operations, and other multi-functional teams to integrate data-driven decision-making into product and business processes.
• Assist with established methods, standard approaches, and promote a data-informed culture within the organization.

What you bring

• Currently enrolled in a full-time, graduate degree program with an expected graduation date in Winter 2027 or Spring 2028.
• Pursuing a graduate degree in a quantitative field such as mathematics, statistics, engineering or natural science.
• Proven expertise in data science, with a strong background in statistical analysis and machine learning.
• Outstanding problem-solving skills and the ability to think critically and creatively.
• Strong programming skills in Python & SQL, or other relevant languages.
• Experience with data visualization tools and techniques.
• Ability to successfully implement projects and compete in a fast-paced environment.
• Previous experience working in financial technology is preferred.