Priscilla O.

Key Skills

Software

Other

Google Cloud Vertex AI

Top Subject Matter

AI training datasets (NER and text classification)

AI training datasets (text classification)

Computer vision training data (image segmentation)

Top Data Types

Text

Image

Video

Top Task Types

Bounding Box

Polygon

Transcription

Classification

Segmentation

Entity (NER) Classification

Freelancer Overview

Data Annotation Specialist (AI Training): NER annotation and dataset quality review. Core strengths include N and A. Education includes Bachelor of Science, Kwame Nkrumah University of Science and Technology (KNUST) and Certification, DataLens Africa. AI-training focus includes data types such as Text and Image and labeling workflows including Entity (NER) Classification, Classification, and Segmentation.

Labeling Experience

Data Annotation Specialist (AI Training): instance segmentation labeling

ImageSegmentation

Completed instance segmentation annotations for computer vision workflows to accurately label individual objects. Applied consistent labeling practices to improve dataset quality and reduce errors. Conducted review and correction of inconsistencies to strengthen overall training readiness. • Labeled image instances for instance segmentation • Reviewed and corrected inconsistent annotations • Maintained guideline-based labeling consistency • Prepared and validated datasets for submission

2025 - 2026

Data Annotation Specialist (AI Training): image segmentation labeling

ImageSegmentation

Performed image segmentation annotations for computer vision workflows, ensuring precise object boundary labeling. Maintained high labeling accuracy by following detailed segmentation guidelines. Validated labeled outputs prior to submission to support dependable training data for AI development. • Annotated images using segmentation labels • Ensured precise object boundary definition • Followed detailed annotation guidelines • Validated segmentation outputs before delivery

2025 - 2026

Data Annotation Specialist (AI Training): text classification labeling and QA

TextClassification

Completed text classification labeling to support accurate categorization of AI training datasets. Ensured label quality by verifying outputs against dataset expectations and project standards. Helped improve model understanding by maintaining reliable and consistent labeled categories. • Labeled text samples for classification tasks • Maintained accuracy through guideline adherence • Verified and corrected labeling issues during QA • Supported dataset validation before submission

2025 - 2026

Data Annotation Specialist (AI Training): NER annotation and dataset quality review

TextEntity (NER) Classification

Annotated text datasets using Named Entity Recognition (NER) to improve entity identification consistency across training data. Followed detailed annotation guidelines to maintain accuracy, consistency, and quality for each project. Reviewed and corrected annotation inconsistencies to reduce labeling errors and support reliable submissions. • Performed NER annotation for entity extraction tasks • Applied guideline-based labeling standards for consistency • Conducted quality review and correction of inconsistencies • Validated annotated datasets prior to submission

2025 - 2026

Final Year Project — Optimization in Large Language Models

OtherText

Researched and evaluated how structured dataset quality and annotation accuracy influence large language model performance. Analyzed patterns in language model outputs to assess output reliability and performance behavior. Applied analytical methods to understand how AI systems process and generate language in the context of LLM optimization. • Evaluated data quality and annotation accuracy impacts • Assessed structured dataset patterns and reliability • Analyzed LLM output behavior for consistency • Applied analytical methods for LLM optimization insights

2025 - 2025

Education