Define and implement scalable data quality measures across complex, multimodal data labeling pipelines
Drive data-centric ML model improvements to achieve critical Zoox milestones
Support an org-wide data ontology and class structure for perception models
Determine trade-offs and integrations between human-labeled, human-in-the-loop, and zero-shot autolabeled data
Build metrics to quantify labeling throughput, capacity, and annotator/vendor quality

Master's or PhD degree in a field relevant to autonomous driving (computer science, robotics) to the analysis of human data (computational neuroscience, cognitive 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
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.
Experience with data-centric ML development and data curation

Senior Data Scientist – Data for Perception Machine Learning

Key skills