Data Analysis & Quality Control (AI training data readiness through auditing)
The experience involves processing and auditing technical datasets to detect anomalies and improve data reliability for analytical and AI-related workflows. It includes checking numeric/physical system metrics for correctness and consistency at fine temporal resolution. It also supports quality control by identifying systemic errors, bias, and inconsistencies in raw datasets. • Audited experimental datasets for metric accuracy (e.g., millisecond-level verification of dynamics). • Calculated and validated results for rotational dynamics and physical system measurements. • Flagged data anomalies, bias, and inconsistency patterns. • Applied structured quality control to optimize overall dataset quality.