Senior Data Scientist, Data Quality and AI Evaluation

Responsibilities

• Define quality metrics and evaluation methods that reveal where auto-labeling pipelines succeed, fail, or regress.
• Build and maintain human-verified golden datasets covering representative driving conditions, critical corner cases, historical failures, and new requirements.
• Analyze large-scale sensor, camera and annotation data to identify root causes, discover systematic errors, and prioritize improvements with perception and auto-labeling engineers.
• Develop automated approaches for failure clustering, similar-case discovery, and targeted selection of data for human correction.
• Build statistically sound audits and experiments, establish release acceptance criteria, and communicate findings through clear dashboards and recommendations.
• Partner with labeling operations to improve annotation guidelines, reviewer consistency, and the balance between quality, efficiency, and cost.
• What We Need To See
• Great foundations in statistics, experimental design, sampling, hypothesis testing, and uncertainty estimation, with practical experience evaluating machine learning systems and data quality.
• Experience working on AI/LLM evaluation with proficiency in Python and SQL, including analyzing large datasets and building reproducible analytical workflows. Understanding of precision, recall, class imbalance, selection bias, data leakage, and evaluation across significant scenario groups.
• Ability to distinguish reference-label defects from model errors and translate ambiguous quality concerns into measurable questions and actionable recommendations.

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