Manager III, AI Science

Intuit

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
Mountain View, CASan Diego, CA
Salary
$264,500–$357,500 / yr
Posted
10 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $209k
This role $311k
$144k most similar roles pay here $380k

This role pays more than 98% of similar roles. Most pay $169,300–$249,640 — the shaded band above. At the midpoint, this role pays about $311k versus about $209k for comparable roles.

Based on 240 similar postings.

Employer

About Intuit

Intuit is a financial software company known for products like TurboTax, QuickBooks, Mint, and Credit Karma, helping consumers and small businesses manage their finances and taxes. Industry: Financial Software & Technology

Intuit currently has 222 open roles on FindRole.

Listed pay typically runs $202,500–$274,000 across 199 roles with salary data.

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At a glance

TL;DR · Manager III, AI Science

Manager 3, AI Science (MLOps, AI, Data Science) joins the Data, Growth & Experiences organization to lead a multi-team AI Science team. This role oversees three interconnected areas: MarTech, Data, and Data Acquisition. You will provide technical direction for building production-grade, agent-native architectures, including self-learning semantic layers, digital-twin models for marketing and sales, and automated anomaly detection systems. The position involves managing a team of Data Scientists and AI Scientists to develop the models, pipelines, and infrastructure required for agents to reason over trusted data. Key responsibilities include shaping technical roadmaps, ensuring governance, and partnering with Product and Engineering teams. Required skills include expertise in Python, ML frameworks, LLMs, and cloud infrastructure like AWS, GCP, or Databricks. The role focuses on solving complex problems regarding data consistency, entity resolution, and the integration of third-party data into a unified knowledge stack.

What you'll do

  • Define the technical roadmap for AI-ready architectures and a self-learning semantic layer to unify business rules and data.
  • Design verification and evaluation layers to ensure accurate outputs for downstream agents and systems.
  • Develop digital-twin models for marketing and sales to enable scenario simulation based on real transactional data.
  • Implement automated anomaly detection across the data estate to identify quality issues, pipeline breaks, and semantic drift.
  • Drive the adoption of internal AI "paved roads" and agentic capabilities across products and platforms.
  • Manage a multi-team organization of Data Scientists and AI Scientists while mentoring senior individual contributors.
  • Partner with Product and Design teams to co-create AI-native solutions that solve specific customer problems.
  • Translate high-level business strategy into scalable delivery systems and execution plans for multiple technical teams.

What we're looking for

  • 10+ years of experience in AI/ML, Data Science, or MLOps with technical depth in algorithmic modeling, production ML systems, or applied data science.
  • 3+ years of people management experience, including managing principals or senior/staff-level ICs across multiple teams.
  • Proven experience shaping technical strategy and architecture for AI/ML systems from data curation through production deployment.
  • Strong hands-on background in MLOps, including building production models, designing data pipelines, and establishing engineering standards.
  • Experience with semantic layers, knowledge graphs, or entity-resolution systems to encode business context as reusable knowledge.
  • Experience building statistical or ML-based anomaly detection for production data systems such as quality monitoring or drift detection.
  • Experience partnering cross-functionally with Product, Design, and Engineering to ship AI-driven customer experiences.
  • Bachelor's degree in Computer Science, Statistics, Data Science, or a related quantitative field; Master's or PhD preferred.

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