Chikwado Idu

I’m a results-driven Data Annotation Specialist focused on labeling and categorizing complex datasets to fuel machine learning development. Over 4 years, I’ve maintained 95%+ accuracy rates while consistently exceeding productivity benchmarks across image, text, audio, and video modalities.

I bring deep expertise in computer vision annotation, including bounding boxes, polygons, semantic segmentation, keypoint annotation, and instance segmentation. I’ve also worked with specialized 3D data such as point cloud (3D) and LiDAR, including point cloud labeling and 3D spatial information for augmented reality and spatial computing applications.

On the NLP side, I perform entity recognition, sentiment analysis, intent classification, and part-of-speech tagging. I’ve also handled dependency parsing and coreference resolution, and conducted sentiment analysis and entity recognition to improve intent classification accuracy.

I’m quality-obsessed and process-minded: QA protocols, peer reviews, data privacy (PII), and guideline refinement are core to how I deliver. I collaborate with project managers and QA teams to clarify ambiguous cases, resolve annotation discrepancies, and document edge cases so datasets stay consistent and reliable.