Elisha E.

Key Skills

Software

Labelbox

Data Annotation Tech

Top Subject Matter

AI model training data annotation (image segmentation and text classification/QA workflows)

Top Data Types

Image

Text

Top Task Types

Segmentation

Freelancer Overview

AI Data Annotator, Data Innovations Nigeria LTD (2022–Present). Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Labelbox. Education includes First School Leaving Certificate, Methodist Primary School (2009) and General Certificate Examination, A.M.E Zion Secondary Commercial School (2013). AI-training focus includes data types such as Image and Text and labeling workflows including Segmentation.

Labeling Experience

AI Data Annotator, Data Innovations Nigeria LTD (2022–Present)

LabelboxImageSegmentation

Responsible for executing complex AI annotation tasks across both image and text datasets to support model development. Maintains high label consistency by identifying and documenting edge cases across diverse data types. Works with the data science team to iteratively refine annotation guidelines and improve overall dataset quality. • Performs image annotation suitable for segmentation workflows (polygons/bitmasks) • Performs text labeling for multiclass text classification and related QA-style edge cases • Ensures consistent annotation by flagging ambiguous cases and applying guideline updates • Uses Labelbox to manage high-volume labeling while meeting project deadlines

2022 - Present

data labelling & annotation specialist

ImageSegmentation

During my tenure at Data Innovations Nigeria LTD, I spearheaded the high-precision annotation of over 50,000 high-resolution images and 100,000 text strings, focusing on large-scale multimodal dataset development for Computer Vision and NLP applications. My primary responsibilities involved executing complex semantic segmentation using polygons and bitmasks, alongside multiclass text classification and Named Entity Recognition (NER) within Labelbox. To ensure industry-leading data integrity, I strictly adhered to consensus-based labeling and "Gold Standard" benchmarks, consistently maintaining an individual quality accuracy score of 98%+ and significantly reducing model training errors through rigorous edge-case detection and peer-review QA workflows.

2025 - 2026

Education

Work History