Jocelyn Huang developed the BIRD Benchmark for text-to-SQL model evaluation in the NVIDIA/NeMo-Skills repository, delivering a standardized workflow for assessing model performance. She designed and implemented data preparation pipelines, evaluation logic, and metrics tracking using Python and SQL, focusing on robust data processing and benchmarking techniques. Her work enabled repeatable, end-to-end evaluation across multiple text-to-SQL models and datasets, supporting data-driven model selection and optimization. By integrating comprehensive evaluation metrics, Jocelyn’s contribution addressed the need for reliable benchmarking in customer deployments, demonstrating depth in data engineering and metrics instrumentation while enhancing the repository’s capabilities for text-to-SQL tasks.