Senior Data Scientist AI Evaluation

• Design AI evaluations: Define ground truth, metrics, and scoring methods for models and agents.
• Build repeatable eval loops: Track quality over time and catch regressions before release.
• Partner on infrastructure: Work with engineering and analytics engineering to operationalize eval harnesses.
• Drive iteration: Translate eval results into actionable recommendations for system improvements.
• Establish quality standards: Set evaluation guidelines, documentation, and review practices.
• Mentor and align: Foster evaluation best practices and build a culture of measurable AI quality across the team.