Principal Data Scientist – Applied AI

Responsibilities

• Partner directly with HR business stakeholders to identify the highest-leverage workflows for AI transformation.
• Design, build, and ship production AI systems including agentic workflows, LLM applications, and supporting infrastructure.
• Build evaluation frameworks that measure model and system performance against real business outcomes.
• Translate ambiguous business problems into prototypes quickly, then iterate to durable production solutions.
• Write production‑grade code and collaborate on engineering practices that take AI from prototype to scaled deployment.
• Mentor team members on AI engineering, system design, and applied research methods.
• Ensure responsible AI practices, data governance, and security standards across the project lifecycle.

• Extensive experience building and deploying AI systems in production, including LLM applications or agentic workflows.
• Strong software engineering fundamentals with proficiency in Python and modern AI tooling and frameworks.
• Hands‑on experience with evaluation methods for AI systems, including offline and online testing approaches.
• Working knowledge of data engineering, database technologies including SQL, and the infrastructure that supports production AI.
• Ability to translate ambiguous business problems into shipped solutions in close partnership with non-technical stakeholders.
• Track record of moving fast from prototype to production while maintaining quality and reliability.
• Effective communication skills to align technical work with business priorities.