Staff Data Scientist - Customer Success & AI Analytics

Intuit; San Diego (CA); On-site; USD 194,000 - 263,000; Full time

Job summary

Intuit is seeking a Staff Data Scientist for the GBSG Customer Success Data Science & Analytics team. You will partner with CS, Services, Product, and Operations to define problems and success metrics, and lead advanced experiments including causal analyses to drive predictive insights and business decisions.

You will build scalable measurement frameworks, develop predictive models and segmentation, and apply AI tooling to accelerate the analytics workflow while maintaining rigor.

Qualifications

• 8+ years in data science, analytics, or product analytics with impact in CS, product, or go-to-market domains.
• Deep foundation in causal inference and quasi-experimental design (DiD, matching, synthetic control, regression discontinuity).
• Predictive modelling and segmentation experience used in production decisions.

Responsibilities

• Serve as strategic analytics partner to CS, Services, Product, and Operations leaders to define problems, metrics, and data-informed decisions.
• Design, execute, and interpret experiments beyond A/B testing, including causal methods to isolate impact of human-assisted motions.
• Develop scalable measurement frameworks for CS and Services outcomes like engagement, retention, and TPV growth.
• Build predictive models and customer segmentation to improve targeting and CSM assignments.
• Apply modern AI tooling to accelerate analytics while maintaining rigor and reproducibility.
• Translate complex analyses into clear, actionable insights for senior leadership and cross-functional partners.

Skills

Data science

Causal inference

Predictive modelling

SQL

Python

AI tooling

Communication

Education

Bachelor's degree in a quantitative field

Advanced degree preferred

Tools

Cursor

Claude

Job description

Intuit is seeking a Staff Data Scientist for the GBSG Customer Success Data Science & Analytics team. You will partner with CS, Services, Product, and Operations to define problems and success metrics, and lead advanced experiments including causal analyses to drive predictive insights and business decisions.

You will build scalable measurement frameworks, develop predictive models and segmentation, and apply AI tooling to accelerate the analytics workflow while maintaining rigor.