Senior Data Scientist, Data Quality and AI Evaluation

NVIDIA•Santa Clara, CA

6d•$224,000 - $431,250

About The Position

NVIDIA is building an AI-native autonomous-driving architecture that integrates classical safety stacks, foundation models, and scalable AI systems. The Senior Data Scientist, Data Quality and AI Evaluation will define how auto-labeling pipelines are evaluated, build reliable human-verified datasets, and use large-scale sensor, camera, and annotation data to identify errors and guide improvements. The role also develops statistical audits and automated methods to find and prioritize cases for human review, establishes release acceptance criteria, and collaborates with engineering and labeling operations to improve quality, consistency, efficiency, and cost.

Requirements

Strong foundations in statistics, experimental design, sampling, hypothesis testing, and uncertainty estimation.
Practical experience evaluating machine-learning systems and data quality.
Experience with AI/LLM evaluation and 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.
Strong communication and teamwork skills, with experience working across engineering, research, product, or operations teams.
12+ years of industry experience and a bachelor’s degree, or a proven track record for a master’s degree, in statistics, mathematics, computer science, engineering, or a related quantitative field, or equivalent practical experience.

Nice To Haves

Background in autonomous driving, robotics, or computer vision, particularly with camera and LiDAR data, 2D/3D annotations, multi-object tracking, occupancy, or temporal and cross-sensor consistency.
Experience designing human-in-the-loop labeling and audit workflows, measuring annotator agreement, or maintaining versioned benchmarks with protected evaluation sets.

Responsibilities

Define quality metrics and evaluation methods to identify success, failure, and regression in auto-labeling pipelines.
Build and maintain human-verified golden datasets spanning representative driving conditions, critical corner cases, historical failures, and new requirements.
Analyze large-scale sensor, camera, and annotation data to identify root causes and 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 dashboards and recommendations.
Partner with labeling operations to improve annotation guidelines, reviewer consistency, and the balance among quality, efficiency, and cost.