Data Scientist, Zoox

• 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 modelingProficiency with Git, unit testing, and collaborative development practices

Desirable

• The Collision Avoidance System (CAS) is responsible for detecting and reacting to imminent collision situations in support of our vehicle’s overall safety goals
• CAS Perception is responsible for processing raw sensor data from our vehicle’s world-class sensor suite using a combination of geometric, interpretable algorithms and deep learning to detect near-collisions with obstacles along our intended driving path, in the most challenging dense urban environments and under tight compute resource constraints
• Overall CAS is parallel and complementary to our Main Artificial Intelligence (AI) autonomy stack, and has a close relationship with our vehicle hardware and safety teams in order to architect redundancy into our overall driving system
• You will define key performance metrics for our safety critical perception and prediction system, in order to guide product development to a commercial launch
• You will work closely with software engineers, data engineers, and data scientists to develop metrics and tools for analyzing errors and guiding improvements in our systems
• You will contribute to all phases of the software development cycle including prototyping, requirements capture, design, implementation, and validation
• You will apply distributed computing algorithms to efficiently analyze petabytes of urban driving data