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# AI Science

- **Company:** Intuit Inc.
- **Location:** Mountain View, CA, United States
- **Experience:** Expert
- **Salary:** $209,500.0 - $283,500.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Linux, Apache Hive, Python (Programming Language), Natural Language Processing, Tensorflow, Scala (Programming Language), SQL Databases, Explainable AI (XAI), Apache Spark, Deep Learning, Technical Debt, Agentic-AI, Information Technology
- **Published:** October 2, 2026
- **Apply:** https://intuit.avature.net/externalCareers/JobApplication?pipelineId=24506

## About the Role

* BS, MS, or PhD in Statistics, Mathematics, Computer Science, Economics, Operations Research, or equivalent work experience.
 * 7+ years of industry experience with AI/data science.
 * 7+ years hands-on expertise in ML paradigms (Causal-ML, supervised/unsupervised, Online, Bayesian, Reinforcement, Deep Learning).
 * Proficient in multiple optimization paradigms, NLP techniques, explainable AI, and ML frameworks.
 * Expertise in Python, Scala, Java, and/or R; efficient in SQL/Hive/SparkSQL; comfortable in Linux.
 * Experience building end-to-end reusable pipelines from data acquisition to model output delivery; strong oral and written communication skills.

## Description

You'll own building AI systems with a durable edge over commoditized AI, architecting systems that deeply leverage proprietary data, instituting comprehensive evaluation and monitoring, and leading research workstreams that produce meaningful publications and technology transfer to product teams., * Stay current with the field and emerging research; recommend novel techniques that materially improve product metrics.

 * Lead creation of reusable, versioned data and feature assets, and set data-quality standards across your area.
 * Craft robust evaluation strategies, including adversarial and edge-case coverage.
 * Lead the design of AI-first and agentic systems, owning the judgment call on when a problem calls for model training rather than prompting; architect multi-stage agent pipelines and reusable agent-driven infrastructure.
 * Lead formulation of multi-component research problems where existing methods fall short, setting methodological standards across research workstreams.

Execution & Delivery

 * Orchestrate complex initiatives by sequencing dependencies, communicating clear estimates, and managing risk across pivots.
 * Identify and eliminate barriers from technical or prioritization complexity; reduce low-quality AI output through oversight and guardrails.
 * Lead complex, ambiguous initiatives, partnering with Product, Analytics, Sales, and other stakeholders; drive end-to-end architecture and hold the team to high standards for code, design, and cost.

Customer Impact

 * Frame customer problems, own customer solutions, and drive product thinking to deliver measurable, impactful value.
 * Connect feedback, data, and usage signals from customers to identify latent needs.
 * Design end-to-end customer workflows and scalable technical solutions, communicating with product, design, and PM partners; own the transfer of research outputs into product or platform contexts., * Elevate the effectiveness of your team, other teams, and cross-functional partners; coach engineers to raise standards.
 * Guide engineers on how to break work into pieces AI agents can execute reliably.
 * Identify larger gaps in tech debt, tooling, and agent fluency that drive meaningful productivity gains.