Principal Data Scientist, Capabilities & Acceleration

Build AI-Native Data Products:

• Design and ship production-grade capabilities that combine data engineering, applied AI, and analytical judgment, moving from prototype to durable system rather than stopping at proof of concept.
• Serve as the first builder on shared capabilities, validating architectural decisions through real implementations and feeding requirements back into the platform.

Architect Data Foundations:

• Design the data architecture, semantic grounding, and pipelines that AI and analytical workloads depend on, ensuring correctness and reuse are properties of the system rather than of individual diligence.
• Establish patterns for how governed metric definitions, data quality, and freshness guarantees flow into downstream products and agentic workflows.

Raise the Engineering Bar for Data Science:

• Define and demonstrate what production-grade looks like for data science work: testing, observability, cost awareness, and graceful failure.
• Partner with domain data science teams to turn one-off solutions into reusable, discoverable assets the broader organization can adopt with minimal effort.

Lead Through Craft:

• Serve as a technical leader and force multiplier across the GBSG Data Science organization, setting direction that others follow through demonstrated work rather than mandate.
• Mentor data scientists on engineering practice, AI-native development, and data architecture, raising the collective capability of the org.

Operate with Agility and Impact:

Rapidly assess where the organization is duplicating effort or blocked by missing foundations, and prioritize the capabilities with the broadest leverage.

Balance durable architecture with fast iteration, shipping usable increments rather than waiting on complete platforms.

Experience & Expertise:

• 10+ years of experience across data science, data engineering, or applied machine learning, with a proven record of shipping production systems rather than analyses alone.
• Demonstrated depth in data architecture and modeling, with direct experience building the data foundations that analytical and AI products depend on.
• Hands-on experience building with modern AI tooling, including LLM-based applications, agentic workflows, and evaluation practices.

Technical Proficiency:

• Advanced skills in SQL and Python, with strong software engineering fundamentals including version control, testing, code review, and CI/CD.
• Practical experience with distributed data processing and warehouse platforms (e.g., Spark, Databricks), pipeline orchestration, and production deployment patterns.
• Familiarity with semantic layers, metric governance, or metadata systems is a strong plus.

Cross-Functional Collaboration & Leadership:

• Strong ability to partner with data scientists as your primary customer, understanding their workflows deeply enough to build capabilities they actually adopt.
• Excellent communication skills, with a track record of influencing technical direction across teams you do not manage., * Genuine enthusiasm for building with AI, paired with the discipline to ensure what gets built rests on solid data foundations.
• A bias toward adoption: measures success by what other teams ship using your work, not by what you ship alone.
• An entrepreneurial mindset suited to a new team, comfortable operating with ambiguity and defining the path as much as walking it.

Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender. The expected base pay range for this position is: Mountain View $247,500 - $335,000

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