Data Scientist, Zoox

• Master's or PhD degree in Statistics, Mathematics, Computer Science, or a related field
• Proficient using data query languages (SQL and/or Spark/scala) to quickly build complex yet efficient data queries at scale and using Python to build production-quality code
• Basic experience with autonomous driving, safety-critical systems, or computer vision systems
• Proficient in exploratory data analysis (EDA) and data visualization to understand and present trends and their implications for the business
• Background in statistical modeling and analysis; including experience making data-driven decisions that connect point and uncertainty estimates to business impact
• You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills

Desirable

• We are seeking a highly skilled and experienced Data Scientist to join our Perception Verification and Validation team
• The team is responsible for verifying and validating the perception stack of our autonomous driving system
• The candidate will work closely with other data scientists, perception engineers, and systems engineers to develop datasets and metrics to quantify the performance of our perception system
• The candidate will also help define key performance metrics that will enable perception engineers to directly measure the impact of new features on both the overall Perception stack as well as end-to-end system behavior
• Define key performance metrics that will help perception engineers to measure the impact of new features on the perception stack and the end-to-end system behavior
• Create and maintain datasets used to evaluate the perception stack and end-to-end driving performance
• Analyze large scale metric results to identify patterns and insights about perception failure modes. Present results and insights to leadership
• Design and implement statistical methods to quantify uncertainty in our performance and validation metrics