Principal Data Scientist — AI Capabilities & Foundations

Intuit, Inc.; San Diego (CA); On-site; USD 248,000 - 335,000; Full time

Job summary

Intuit, Inc. is seeking a Principal Data Scientist for the Global Business Solutions Group (GBSG) Data Science organization in Mountain View, CA.

The role centers on building AI-native data products, establishing robust data foundations, and raising the engineering bar for data science through rigorous testing, observability, and scalable deployment. You will lead with impact, mentor other data scientists, and collaborate with cross-functional teams to deliver reusable capabilities across

Qualifications

• 10+ years of experience across data science, data engineering, or applied machine learning with a proven record of shipping production systems
• Demonstrated depth in data architecture and modeling for AI and analytics products
• Hands-on experience with modern AI tooling, including LLM-based applications and agentic workflows

Responsibilities

• Build AI-native data products by shipping durable, production-grade capabilities
• Architect data foundations including data architecture, semantic grounding, and data quality patterns
• Raise the engineering bar for data science with testing, observability, and cost awareness
• Lead through craft as a technical leader, mentoring engineers and scientists
• Operate with agility to prioritize capabilities with broad organizational impact

Skills

SQL

Python

Data modeling

Communication

Data architecture

Tools

Spark

Databricks

CI/CD

LLM tooling

Job description

Intuit, Inc. is seeking a Principal Data Scientist for the Global Business Solutions Group (GBSG) Data Science organization in Mountain View, CA.

The role centers on building AI-native data products, establishing robust data foundations, and raising the engineering bar for data science through rigorous testing, observability, and scalable deployment. You will lead with impact, mentor other data scientists, and collaborate with cross-functional teams to deliver reusable capabilities across