Worked on the NVIDIA/garak repository to optimize the evaluations analytics pipeline by streamlining DataFrame construction. Used Python and pandas to directly assign the output of pd.DataFrame.from_dict(evals) to self.evaluations, removing an unnecessary intermediate copy and reducing memory usage in evaluation workflows. Reformatted the score column calculation to improve code readability and maintainability, supporting easier future modifications. Focused on performance optimization and memory management, the changes provided a cleaner data transformation path without introducing new bugs. Demonstrated a methodical approach to data analysis and commit tracing, delivering a targeted feature that enhanced the efficiency of the existing data processing pipeline.