Principal Applied Scientist
Core
Lead the development of metrics and evaluation methodologies to measure the quality, accuracy, and usability of AI systems, designing experiments and building analytical tools for continuous quality monitoring.
Role type
Principal Applied Scientist (AI Evaluation & Experimentation)
Builds
Scalable data pipelines, dashboards, and analytical tools for AI quality monitoring
Domain
Artificial Intelligence, Statistics, Data Science
Deliverable
production ML models
Required skills
Statistical analysis, Machine learning, Data mining, Experiment design, Root cause analysis, Technical mentoring, Data-driven decision making, Research program leadership, Production system deployment, Product lifecycle management
Preferred skills
None stated
Technologies
None stated
Responsibilities
Design and analyze online and offline experiments to quantify product improvements; Build scalable data pipelines and dashboards for continuous quality monitoring; Investigate quality regressions and propose measurable improvements; Shape best practices for AI evaluation and responsible AI measurement; Mentor scientists in adopting evaluation methodologies; Partner with engineering and product teams to prioritize investments using data-driven recommendations.
Seniority
Principal, strategy & mentorship
