Principal Data Scientist, Capabilities & Acceleration

• The Global Business Solutions Group (GBSG) Data Science organization is building a central capability team dedicated to how Data Scientists across the org build, ship, and scale their work.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• Mountain View $247,500 - $335,000

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