Grace Ilavonga

Key Skills

Software

Data Annotation Tech

Top Subject Matter

No subject matter listed

Top Data Types

Image

Top Label Types

Bounding Box

Polygon

Point Key Point

Segmentation

Freelancer Overview

I am a detail-oriented data annotator with 7 years of experience providing high-quality labeled datasets for machine learning and AI projects. My expertise spans image, text, audio, and video annotation, including tasks such as bounding boxes, polygons, semantic segmentation, and NLP assignments like named entity recognition and sentiment analysis. I am skilled in using tools like Labelbox, SuperAnnotate, and various internal platforms, always ensuring strict adherence to guidelines and maintaining top-tier data quality. I thrive in fast-paced, quality-driven environments, consistently meeting productivity targets and collaborating with teams to resolve complex annotation challenges. My commitment to accuracy and reliability has earned me recognition for handling sensitive and complex projects, and I am dedicated to supporting the development of robust AI systems through meticulous data preparation.

Labeling Experience

associate

Data Annotation TechImageBounding BoxPolygon

Data Labeling Tasks Performed I performed detailed annotation tasks including image and video labeling using bounding boxes, polygons, and semantic segmentation; text annotation such as classification, sentiment analysis, and named entity recognition; and audio transcription and labeling. The work also involved resolving edge cases, applying consistent labeling standards, and updating annotations based on feedback or revised guidelines. Project Size The project was large-scale, involving hundreds of thousands to millions of data points across multiple phases. Tasks were completed both individually and as part of a team, with daily and weekly productivity targets to meet tight delivery timelines. Quality Measures Adhered To I adhered strictly to defined quality standards, including maintaining high accuracy scores, passing multi-level quality audits, and following detailed annotation guidelines. Quality was ensured through self-checks, peer reviews, and reviewer feedback loops. I c

Data Labeling Tasks Performed I performed detailed annotation tasks including image and video labeling using bounding boxes, polygons, and semantic segmentation; text annotation such as classification, sentiment analysis, and named entity recognition; and audio transcription and labeling. The work also involved resolving edge cases, applying consistent labeling standards, and updating annotations based on feedback or revised guidelines. Project Size The project was large-scale, involving hundreds of thousands to millions of data points across multiple phases. Tasks were completed both individually and as part of a team, with daily and weekly productivity targets to meet tight delivery timelines. Quality Measures Adhered To I adhered strictly to defined quality standards, including maintaining high accuracy scores, passing multi-level quality audits, and following detailed annotation guidelines. Quality was ensured through self-checks, peer reviews, and reviewer feedback loops. I c

2018

Education

Work History