Intuit
Manager 3, Data Science - Data Foundations
$234K–$316K
Mountain View, California +1 · Manager · Full-time · Posted Oct 5, 2026
- Location
- Mountain View, California
San Diego, California - Experience
- 14+ years in data science, analytics, or data platform work, including 5+ years directly managing data scientists or analytics engineers.
- Education
- BS or MS in Statistics, Mathematics, Computer Science, Economics/Econometrics, Operations Research, Engineering, or a related quantitative field.
About the job
Leads Intuit Customer Success Data Science & Analytics’ Data Foundations team, building governed data products and semantic layers while advancing AI-driven data operations.
What you'll do
- Lead and develop a team of data scientists; own hiring, performance management, and technical standards.
- Set the portfolio strategy, roadmap, success criteria, and financial case, and influence senior stakeholders.
- Build governed data foundations and customer journey products; lead data quality, compliance, security, and AI-enabled automation.
What they ask for
- BS or MS in a quantitative field listed in the posting.
- 14+ years in data science, analytics, or data platform work, including 5+ years directly managing data scientists or analytics engineers.
- Strong SQL and Python foundation; experience with modern cloud data platforms, pipeline orchestration, and legacy analytics migrations.
- Demonstrated enterprise-scale data foundation leadership, AI/GenAI or agentic automation experience, and ownership of a multi-million-dollar impact portfolio.
Nice to have
- Experience with data governance and compliance regimes, semantic or knowledge layers, experimentation and causal inference, or Customer Success, customer experience, contact center, or FinTech analytics.
- Experience establishing cross-functional governance mechanisms such as schema/event registries or data stewardship.
Pay
Mountain View: $234,000 - $316,500 (base salary)
Tools
SQL, Python
AI focus
Leads custom AI/GenAI capabilities and agentic tools for data pipeline development and migration, data-quality detection, root-cause analysis, and insight generation; guides measurement of AI-native experiences.
Model-written summary · the original posting is authoritative