Zoox
From Zoox's careers page
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We are seeking a Senior Data Scientist to join our Perception Verification and Validation team.
We are seeking a Senior 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 by building the metrics, datasets, and analyses that tell us how perception performance translates into driving behavior. As a senior member of the team, you will own the evaluation strategy for major perception capabilities from start to finish, partnering with perception and ML engineers, systems and safety engineers, and autonomy leadership to decide what to measure, how to measure it with statistical rigor, and what the results mean for release decisions. Your work will directly shape how Zoox safely and efficiently ships perception improvements to our fleet, connecting model-level performance to end-to-end system behavior. You will also help raise the bar for the team by mentoring other data scientists and setting technical direction for how we validate perception. In this role, you will... Lead the design of metrics and evaluation frameworks for new perception capabilities, partnering with perception engineers early in model development to define what "ready to ship" means and how it connects to end-to-end driving behavior. Drive cross-team validation projects, bringing together perception, systems, safety, and autonomy stakeholders to build consensus on metrics, evaluation datasets, and release criteria. Develop rigorous statistical methods for uncertainty quantification, error estimation, and error propagation (frequentist, bootstrap, and Bayesian) so teams know when changes in performance are real. Build evaluation datasets and analyze large-scale, highly geospatial fleet and simulation data to uncover perception failure modes, then turn findings into clear recommendations for engineering and executive leadership. Mentor and guide a small group of data scientists by defining tasks, reviewing their work, and raising the team's technical bar.
Master's degree with 7+ years of experience, or PhD with 3+ years of experience, in data science, applied science, or a related quantitative role (Statistics, Mathematics, Computer Science, or a related field).
Expert proficiency with SQL and/or Spark (PySpark or Scala) and Python, with a track record of building production-quality analyses and pipelines on large-scale (terabyte+) distributed data.
Deep expertise in statistical modeling, inference, and decision making, including uncertainty quantification, and a history of connecting point and uncertainty estimates to high-stakes decisions.
Experience designing metrics and validating ML models, such as computer vision, sensor-based, or perception models, including discovering and measuring failure modes.
Experience analyzing geospatial data and/or working with embeddings (e.g., similarity search, representation learning).
Skilled at using AI tools to improve development efficiency and quality.
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