Data Scientist, Core Data - Phd (2026)

Figma Figma · Enterprise · United States · Engineering

Research-minded Data Scientist to join the Core Data team, focusing on building foundational platforms for data science, including experimentation, analytics, and AI tooling. The role involves advancing the experimentation platform, developing ML-based analytical systems, and measuring AI-powered features using causal inference and statistical modeling.

What you'd actually do

  1. Partner across teams to define and track important metrics, develop experiments, and uncover insights that inform strategic decisions
  2. Accelerate Figma's experimentation platform and methodology, including A/B testing frameworks and causal inference techniques
  3. Construct models and analytical frameworks based on machine learning to support product, platform, and business initiatives
  4. Create tools, datasets, and systems that enable others to work with data more efficiently and rigorously
  5. Complete and own complex data projects end-to-end, from problem prioritisation to solution delivery
  6. Drive data quality, accessibility, and the democratization of data across the organization

Skills

Required

  • PhD in a quantitative field (Statistics, Computer Science, Economics, Operations Research, Physics, or related)
  • strong foundation in statistical methods, experimentation, and/or machine learning
  • Fluency in SQL
  • proficiency in a scripting language like Python or R
  • exposure to distributed data systems (e.g. Snowflake) through research or internships
  • Ability to communicate technical concepts clearly to both technical and non-technical audiences
  • A curious and rigorous mindset

Nice to have

  • Publications or research experience in experimentation or applied ML
  • industry internship experience applying data science to product or business problems
  • An AI-native mindset, with exposure to or interest in LLM analytics, AI product measurement, or evaluating the impact of AI-powered features
  • A self-starter attitude
  • ability to thrive in ambiguous and fast-paced environments

What the JD emphasized

  • PhD level depth
  • PhD in a quantitative field
  • measure AI-powered features

Other signals

  • developing machine learning-based analytical systems
  • craft how we measure AI-powered features through causal inference and statistical modeling
  • partnering closely with Data Infra, ML, and Applied Science to evolve our platforms and embed AI into the daily workflows of data scientists