Manager 3, AI Science (MLOps, AI, Data Science) at Intuit

Compensation

$264,500 – $357,500/yr

Location: Mountain View, California · San Diego, California
Type: Full-time
Level: lead/mgmt
Posted: Sep 22, 2026

Description

Intuit is hiring a Manager 3, AI Science to lead a multi-team AI Science organization within its Data, Growth & Experiences group. The role owns technical direction for MarTech, data governance and semantic modeling, and third-party data acquisition; leads Data Scientists and AI Scientists on models, pipelines, semantic infrastructure, anomaly detection, digital-twin simulations, and agentic AI systems; and manages people, strategy, delivery, and cross-functional partnerships. The position requires 10+ years of AI/ML, Data Science, or MLOps experience, 3+ years of people management, and a bachelor's degree in a quantitative or related field, with a Mountain View base-pay range of $264,500 to $357,500.

Required Skills

MLOps
AI
Data Science
Python
ML models
LLMs
Semantic layers
Knowledge graphs
Entity resolution
Anomaly detection
Reinforcement learning
AWS
GCP
Databricks

Additional Information

Minimum Education: Bachelor's degree in Computer Science, Statistics, Data Science, or a related quantitative field
Required Previous Experiences: 10+ years of experience in AI/ML, Data Science, or MLOps
3+ years of people management experience
Experience shaping technical strategy and architecture for AI/ML systems at scale
Hands-on experience in ML Operations
Experience with semantic layers, knowledge graphs, or entity-resolution systems
Experience building statistical or ML-based anomaly detection for production data systems
Experience partnering with Product, Design, and Engineering to ship AI-driven customer experiences
Preferred Qualifications: Multiple years of experience applying advanced analytics techniques, including Python, ML models, and LLMs
Experience with generative AI, LLM-based systems, or agentic AI architectures
Experience with reinforcement learning, fine-tuning, or prompt optimization techniques
Experience building simulation or digital-twin models
Experience with self-learning or continuously-updating data systems
Experience with third-party data integration and standardization
Track record of building and scaling AI Science or Data Science teams in a large, matrixed technology organization
Experience driving organization-wide platform adoption