Staff Data Scientist focusing on designing evaluation frameworks to validate safety-critical AI systems. Collaborating with engineers to develop scalable data pipelines and deliver data-driven insights to leadership.
Responsibilities
Design Evaluation Frameworks: Architect statistical methodologies for safety-critical AI systems to form objective, rigorous conclusions about their performance and reliability.
Conduct Robust Analysis: Deliver validation evidence to support increasingly complex operations and identify potential edge-case failures.
Inform Strategy: Deliver clear, data-driven insights to development teams to guide system improvement, and to executive leadership to inform milestone-level go/no-go decisions.
Define Metrics: Drive alignment across engineering teams on performance metrics and data extraction strategies.
Lead the Lifecycle: Manage all phases of evaluation including prototyping, requirements capture, design, implementation, and validation.
Scale Pipelines: Partner with engineers to build and maintain scalable data processing and simulation pipelines, applying distributed computing to analyze petabytes of driving data.
Requirements
MS or PhD in Statistics, Computer Science, Machine Learning, Applied Mathematics, or related quantitative field
Proficiency in Python and SQL with experience in production-quality code
Demonstrated expertise in statistical methodologies including hypothesis testing, power analysis, spatiotemporal modeling, Bayesian inference, and multivariate analysis.
Experience with large-scale data analysis and statistical modeling
Proficiency with Git, unit testing, and collaborative development practices
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