Osawe P.

Key Skills

Software

iMerit

Mercor

Micro1

OneForma

Other

Top Subject Matter

Machine learning training data annotation (text classification and NER)

Operational data cleaning and dataset validation for annotation readiness

Top Data Types

Text

Video

Audio

Top Task Types

Polygon

Classification

Text Generation

Question Answering

Transcription

Evaluation/Rating

Prompt + Response Writing (SFT)

Freelancer Overview

Data Labeling & Annotation Project — Independent / Self-Initiated. Brings 4+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Python (pandas) and Excel. Education includes Master of Science, Federal University of Petroleum Resources (FUPRE) (2025) and Bachelor of Science, University of Benin (2021). AI-training focus includes data types such as Text and labeling workflows including Entity (NER), Evaluation, and Rating.

Labeling Experience

Data Labeling & Annotation Project — Independent / Self-Initiated

Text

Annotated structured and unstructured datasets for text classification, entity labeling, and categorical tagging aligned to ML training objectives. Applied detailed annotation guidelines consistently across hundreds of records while maintaining high inter-annotator agreement standards. Pre-processed raw data using Python (pandas) to flag inconsistencies, duplicates, and out-of-scope entries for QA review. • Interpreted annotation guidelines and ensured labeling consistency • Documented edge cases and ambiguities into a reference guide • Reviewed and validated dataset quality prior to downstream use • Supported ML training objectives through accurate, structured labels

2024 - Present

FRSC Operational Data Cleaning & Reporting — Federal Road Safety Corps

Text

Cleaned, merged, and validated operational FRSC records using repeatable Python/pandas pipelines that are directly analogous to preparing datasets for annotation workflows. Resolved data entry errors and structural inconsistencies across multiple data sources to improve downstream report accuracy. Tracked data quality metrics via an Excel dashboard to surface recurring issues and inform process corrections. • Automated data cleaning and structural validation steps • Identified and corrected inconsistencies affecting annotation readiness • Monitored recurring data quality problems for continuous improvement • Produced structured outputs suitable for label QA and reporting

2023 - Present

Education

Work History