Senior AI Science Leader — MLOps & Semantic Data Platform

Intuit; San Diego (CA); On-site; USD 265,000 - 358,000; Full time

Job summary

Intuit is seeking a Manager 3, AI Science to lead a multi-team AI Science organization within the DGX group. You will drive the technical direction across MarTech, Data, and IDX pipelines, shaping AI-native architectures and governance.

This hands-on leadership role requires strong ML Ops and production ML experience, with collaboration across Product and Engineering to deliver AI-driven customer experiences.

Qualifications

• 10+ years of experience in AI/ML, Data Science, or MLOps with hands-on depth across at least one core area.
• 3+ years of people management overseeing principals or senior ICs across multiple teams.
• Strong background using Python, ML models, LLMs, and related analytics techniques.

Responsibilities

• Shape technical direction across DGX capabilities, aligning architecture and roadmaps into cohesive, AI-ready systems.
• Own the science behind a self-learning semantic layer and verification of outputs before reaching decision-makers or agents.
• Design verification/eval layers to ensure semantic outputs are checked before downstream use, with feedback loops for improvement.
• Lead development of digital-twin models for marketing and sales for scenario simulation and recalibration against real data.
• Drive anomaly detection across data quality, lineage, and semantic context to prevent GTM or agent issues.
• Promote adoption of Intuit's AI/GenAI paved roads and empower agent-driven development across systems.

Skills

AI/ML

Data Science

MLOps

Python

LLMs

Anomaly detection

Knowledge graphs

Product collaboration

Education

Bachelor's degree in CS/Stats/DS

Master's/PhD preferred

Tools

AWS

GCP

Databricks

Job description

Intuit is seeking a Manager 3, AI Science to lead a multi-team AI Science organization within the DGX group. You will drive the technical direction across MarTech, Data, and IDX pipelines, shaping AI-native architectures and governance.

This hands-on leadership role requires strong ML Ops and production ML experience, with collaboration across Product and Engineering to deliver AI-driven customer experiences.