Data Scientist — Experimentation & AI Platform

Nubeero Limited; San Francisco, Northern (CA, KY); Hybrid; USD 170,000 - 178,000; Full time

Benefits offered by this job

Equity

Health, dental & vision

Retirement plan

Parental leave

Paid time off

Mental health support

Job summary

Figma is seeking a research‑minded Data Scientist to join the Core Data team. You will advance experimentation, analytics, and AI tooling that power product teams, partnering with Data Infra, ML, and Applied Science to embed AI in daily workflows.

You will apply PhD‑level depth to real‑world problems, developing causal inference models and measurement approaches for AI‑powered features while ensuring data quality and accessibility across the organization.

Qualifications

• PhD in a quantitative field with strong statistical methods, experimentation, or ML background.
• Fluency in SQL and Python or R; experience with distributed data systems (e.g., Snowflake).
• Ability to communicate complex concepts to technical and non‑technical audiences.
• Curious, rigorous, and able to translate research into real-world impact.

Responsibilities

• Define and track key metrics, design experiments, and extract insights for strategic decisions.
• Advance experimentation platform and causal inference methods at scale.
• Build ML‑powered analytical systems to support product and business initiatives.
• Create data tools and datasets to enable rigorous data work across teams.
• Lead end‑to‑end data projects from problem framing to delivery.

Skills

PhD in quantitative field

SQL

Python

R

Statistical modeling

Experimentation

Communication

Causal inference

Education

PhD in Statistics / CS / Economics / OR / Physics

Tools

Snowflake

Job description

Figma is seeking a research‑minded Data Scientist to join the Core Data team. You will advance experimentation, analytics, and AI tooling that power product teams, partnering with Data Infra, ML, and Applied Science to embed AI in daily workflows.

You will apply PhD‑level depth to real‑world problems, developing causal inference models and measurement approaches for AI‑powered features while ensuring data quality and accessibility across the organization.