<div><p>Zoox is on an ambitious journey to develop a full-stack autonomous vehicle system for cities. We are seeking a Staff Data Scientist to join a verification and validation team that evaluates safety-critical AI systems.</p><p>You will join a team of software and data engineers that leverage methods including log data analysis, simulation, and closed-course structured testing. You'll work cross-functionally with AI software, System Design and Mission Assurance, Simulation, Sensors, and other teams to develop, execute, and iterate on validation methods and pipelines. These pipelines evaluate safety-critical systems, are highly visible, and are an important critical path element of launching our service. The ideal candidate brings a hybrid of statistical rigor and engineering mindset to drive clarity from ambiguity, establish new processes, and propel the team forward. This is a deeply technical and hands-on role where you will be expected to be a self-sufficient builder and coder, not just a manager of projects. </p></div><br><br><h3>In this role, you will: </h3><ul><div><li><p><strong>Design Evaluation Frameworks: </strong>Architect statistical methodologies for safety-critical AI systems to form objective, rigorous conclusions about their performance and reliability.</p></li><li><p><strong>Conduct Robust Analysis: </strong>Deliver validation evidence to support increasingly complex operations and identify potential edge-case failures.</p></li><li><p><strong>Inform Strategy:</strong> Deliver clear, data-driven insights to development teams to guide system improvement, and to executive leadership to inform milestone-level go/no-go decisions.</p></li><li><p><strong>Define Metrics:</strong> Drive alignment across engineering teams on performance metrics and data extraction strategies.</p></li><li><p><strong>Lead the Lifecycle:</strong> Manage all phases of evaluation including prototyping, requirements capture, design, implementation, and validation.</p></li><li><p><strong>Scale Pipelines:</strong> Partner with engineers to build and maintain scalable data processing and simulation pipelines, applying distributed computing to analyze petabytes of driving data.</p></li></div></ul><h3>Qualifications: </h3><ul><li>MS or PhD in Statistics, Computer Science, Machine Learning, Applied Mathematics, or related quantitative field</li><li>Proficiency in Python and SQL with experience in production-quality code</li><li>Demonstrated expertise in statistical methodologies including hypothesis testing, power analysis, spatiotemporal modeling, Bayesian inference, and multivariate analysis.</li><li>Experience with large-scale data analysis and statistical modeling</li><li>Proficiency with Git, unit testing, and collaborative development practices</li></ul><h3>Bonus Qualifications: </h3><ul><div><li><p>Hands-on experience with production machine learning pipelines: dataset creation, training frameworks, metrics pipelines</p></li><li><p>Experience with modern data processing technologies such as Apache Spark, Spark SQL, and Databricks</p></li><li><p>Experience with designing metrics and delivering actionable insights that drive  business decisions</p></li></div></ul><br>

<p>Zoox is developing the first fully electric autonomous vehicles to support low-carbon transportation and to decrease congestion and reliance on individual car ownership in urban areas.&nbsp;</p>

Staff Data Scientist - Verification & Validation

<div><p>Zoox is on an ambitious journey to develop a full-stack autonomous vehicle system for cities. We are seeking a Staff Data Scientist to join a verification and validation team that evaluates safety-critical AI systems.</p><p>You will join a team of software and data engineers that leverage methods including log data analysis, simulation, and closed-course structured testing. You'll work cross-functionally with AI software, System Design and Mission Assurance, Simulation, Sensors, and other teams to develop, execute, and iterate on validation methods and pipelines. These pipelines evaluate safety-critical systems, are highly visible, and are an important critical path element of launching our service. The ideal candidate brings a hybrid of statistical rigor and engineering mindset to drive clarity from ambiguity, establish new processes, and propel the team forward. This is a deeply technical and hands-on role where you will be expected to be a self-sufficient builder and coder, not just a manager of projects. </p></div><br /><br /><h3>In this role, you will: </h3><ul><div><li><p><strong>Design Evaluation Frameworks: </strong>Architect statistical methodologies for safety-critical AI systems to form objective, rigorous conclusions about their performance and reliability.</p></li><li><p><strong>Conduct Robust Analysis: </strong>Deliver validation evidence to support increasingly complex operations and identify potential edge-case failures.</p></li><li><p><strong>Inform Strategy:</strong> Deliver clear, data-driven insights to development teams to guide system improvement, and to executive leadership to inform milestone-level go/no-go decisions.</p></li><li><p><strong>Define Metrics:</strong> Drive alignment across engineering teams on performance metrics and data extraction strategies.</p></li><li><p><strong>Lead the Lifecycle:</strong> Manage all phases of evaluation including prototyping, requirements capture, design, implementation, and validation.</p></li><li><p><strong>Scale Pipelines:</strong> Partner with engineers to build and maintain scalable data processing and simulation pipelines, applying distributed computing to analyze petabytes of driving data.</p></li></div></ul><h3>Qualifications: </h3><ul><li>MS or PhD in Statistics, Computer Science, Machine Learning, Applied Mathematics, or related quantitative field</li><li>Proficiency in Python and SQL with experience in production-quality code</li><li>Demonstrated expertise in statistical methodologies including hypothesis testing, power analysis, spatiotemporal modeling, Bayesian inference, and multivariate analysis.</li><li>Experience with large-scale data analysis and statistical modeling</li><li>Proficiency with Git, unit testing, and collaborative development practices</li></ul><h3>Bonus Qualifications: </h3><ul><div><li><p>Hands-on experience with production machine learning pipelines: dataset creation, training frameworks, metrics pipelines</p></li><li><p>Experience with modern data processing technologies such as Apache Spark, Spark SQL, and Databricks</p></li><li><p>Experience with designing metrics and delivering actionable insights that drive  business decisions</p></li></div></ul><br />

Staff Data Scientist - Verification & Validation

About the Job

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Zoox

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