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Senior/Staff Data Scientist, Quantitative Risk Assessment

Zoox

Foster City, Ca
Hybride
Publié le

Description du poste

Zoox is on an ambitious journey to develop a full-stack autonomous mobility solution for cities and safely deploy such a robotaxi solution. Zoox’s System Design and Mission Assurance (SDMA) team is responsible for constructing the safety case for each milestone. We play a foundational role for the success of the company. As a data scientist focused on quantitative risk assessment, you will help Zoox to improve and expand our existing safety risk assessment framework. The safety risk assessment framework is a critical part of the overall safety case and informs the decision-making in every aspect of the technology. Zoox is on an ambitious journey to develop a full-stack autonomous mobility solution for cities and safely deploy such a robotaxi solution. Zoox’s System Design and Mission Assurance (SDMA) team is responsible for constructing the safety case for each milestone. We play a foundational role for the success of the company. As a data scientist focused on quantitative risk assessment, you will help Zoox to improve and expand our existing safety risk assessment framework. The safety risk assessment framework is a critical part of the overall safety case and informs the decision-making in every aspect of the technology. Lead efforts to improve the fidelity of Zoox’s safety and progress performance metrics Apply distributed computing algorithms to efficiently analyze petabytes of urban driving and vehicle testing data Develop and standardize best practices of safety risk assessment across the company Develop and automate the tooling of the the risk assessment framework Contribute to the improvement and evolution of the Safety Case of Zoox technology, in close collaboration with cross-functional teams including Software, Hardware, Vehicle Development, Fleet Operations, Safety Strategy and Operations, Legal, etc. M.S. or higher degree in an Engineering or Science discipline with a strong focus on Statistics, Probability Theory, or Data Science Proficiency in quanti