PhD Intern, Data Science (2027)

What you’ll do at Figma:

• Partner with cross-functional teams to turn ambiguous product, platform, or business questions into well-defined data science problems
• Analyze user, product, or business data to uncover insights and recommend actions
• Design and evaluate experiments, metrics, statistical or machine learning models, and analytical frameworks
• Communicate assumptions, tradeoffs, limitations, and recommendations clearly to technical and non-technical partners
• Own a focused internship project end-to-end, from problem framing and technical execution through final recommendations, with potential to continue toward publication after the internship

We’d love to hear from you if you have:

• Currently pursuing a PhD in Data Science, Statistics, Computer Science, Human-Computer Interaction (HCI), Economics, Operations Research, Physics, Applied Mathematics, or a related quantitative field; with demonstrated experience conducting independent research (e.g. thesis or equivalent research milestone)
• Demonstrated fluency in one or more research methodologies or technical areas, such as statistics, experimentation, machine learning, causal inference, econometrics, optimization, user research methods, or AI/LLMs
• Experience using a scripting language such as Python or R, as well as proficiency with SQL, for analysis, modeling, or work with complex or large-scale datasets through research, internships, or applied projects
• Ability to explain technical concepts clearly and connect analysis to decisions, recommendations, or product/business impact
• A curious, rigorous, and self-starting mindset, with the ability to thrive in ambiguous, fast-moving environments and translate research into real-world impact

While it's not required, it's an added plus if you also have:

• Prior industry or applied research experience with experimental design, causal inference, forecasting, product measurement, user research, or applied machine learning to influence product, business, platform, or stakeholder decisions
• Publications or research relevant to applied data science
• Experience or interest in AI product measurement, LLM analytics/evaluation, recommendation systems, search, personalization, or evaluating AI-powered features