Rodgers Agao

Key Skills

Software

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Text

Top Label Types

Classification

Object Detection

Text Summarization

Freelancer Overview

I have experience working with data in finance and accounting environments, where accuracy, structure, and attention to detail are critical skills that directly translate into high-quality data labeling and AI training support. My background includes reviewing financial documents, reconciling datasets, classifying transactions, validating figures, and ensuring consistency across reports. This has strengthened my ability to follow detailed guidelines, maintain annotation standards, and detect inconsistencies or edge cases in structured and unstructured data. I am highly analytical, comfortable working with large datasets, and proficient in tools such as Excel and digital platforms used for data review and validation. My experience preparing reports, interpreting financial information, and adhering to strict compliance requirements has trained me to deliver precise, well documented work within deadlines. I bring strong English communication skills, critical thinking, and a disciplined approach to quality control qualities that are essential in AI training data annotation and model improvement projects.

Labeling Experience

AI Training Data Annotation – NLP & Computer Vision Projects

Scale AITextClassificationObject Detection

Worked on multiple AI training data projects involving both NLP and computer vision tasks. Annotated and validated over 20,000+ data points including short-form text, search queries, product reviews, and real-world images. For NLP projects, I performed sentiment analysis, intent classification, and Named Entity Recognition (NER), ensuring precise span tagging and strict adherence to annotation guidelines. Maintained over 95% quality accuracy scores based on internal audits and reviewer feedback. For computer vision projects, I conducted object detection and bounding box annotations for datasets involving vehicles, pedestrians, and retail products using CVAT and similar platforms. Carefully handled edge cases such as occlusions, low-light images, and overlapping objects to improve model training performance. Additionally, I participated in quality assurance (QA) reviews, resolving annotation inconsistencies and improving inter-annotator agreement.

Worked on multiple AI training data projects involving both NLP and computer vision tasks. Annotated and validated over 20,000+ data points including short-form text, search queries, product reviews, and real-world images. For NLP projects, I performed sentiment analysis, intent classification, and Named Entity Recognition (NER), ensuring precise span tagging and strict adherence to annotation guidelines. Maintained over 95% quality accuracy scores based on internal audits and reviewer feedback. For computer vision projects, I conducted object detection and bounding box annotations for datasets involving vehicles, pedestrians, and retail products using CVAT and similar platforms. Carefully handled edge cases such as occlusions, low-light images, and overlapping objects to improve model training performance. Additionally, I participated in quality assurance (QA) reviews, resolving annotation inconsistencies and improving inter-annotator agreement.

2024 - 2024

Education

Work History