Figma
PhD Intern, Data Science (2027)
San Francisco, CA • New York, NY +1 · Internship · Posted Sep 21, 2026
- Location
- San Francisco, CA • New York, NY
US - Education
- 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.
About the job
Own an end-to-end data science research project, analyzing product, user, or business questions and translating findings into recommendations for Figma’s products, strategy, or data science practices.
What you'll do
- Turn ambiguous questions into well-defined data science problems and analyze data to identify insights and recommend actions.
- Design and evaluate experiments, metrics, statistical or machine learning models, and analytical frameworks.
- Communicate findings and project recommendations to technical and non-technical partners; potentially continue toward publication.
What they ask for
- Currently pursuing a PhD in a quantitative field and have demonstrated independent research experience.
- Fluency in at least one relevant methodology or technical area, such as statistics, experimentation, machine learning, causal inference, econometrics, optimization, user research methods, or AI/LLMs.
- Experience using Python or R and proficiency with SQL for analysis, modeling, or complex or large-scale datasets.
- Ability to explain technical concepts and connect analysis to decisions or product/business impact.
Nice to have
- Prior industry or applied research experience in relevant data science areas; 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.
Pay
$68 — $68 USD hourly base pay
Tools
Python, R, SQL
AI focus
May apply AI/LLMs or machine learning; preferred interests include AI product measurement, LLM analytics/evaluation, recommendation systems, search, personalization, and evaluating AI-powered features.
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