Segun A.

Key Skills

Software

Don't disclose

Top Subject Matter

AI evaluation and supervised data annotation (sentiment analysis, NER)

AI evaluation for annotation quality control

Legal Services & Contract Review

Top Data Types

Text

Document

Top Task Types

Entity (NER) Classification

Freelancer Overview

AI Evaluation & Data Operations Specialist (Independent/Contract Projects). Brings 12+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Don't disclose. Education includes Bachelor of Science, Obafemi Awolowo University and Secondary School Leaving Certificate, Eleyele Secondary School (2015). AI-training focus includes data types such as Text and labeling workflows including Entity (NER) Classification, Evaluation, and Rating.

Labeling Experience

AI evaluation component of AI Evaluation & Data Operations Specialist work

Don't discloseText

Conducted systematic AI model evaluation to verify output quality against defined criteria for the annotation workflow. Provided detailed feedback on model performance to guide model optimization and inform iterative improvements. Ensured evaluation standards remained consistent across batches of labeled data. • Evaluated AI outputs using predefined criteria • Produced detailed feedback to support performance optimization • Supported iterative refinement based on evaluation results • Maintained consistent evaluation standards across data batches

2023 - Present

AI Evaluation & Data Operations Specialist (Independent/Contract Projects)

Don't discloseTextEntity (NER) Classification

Applied prompt engineering and AI evaluation techniques to support text and data annotation deliverables while maintaining consistent quality standards. Performed sentiment analysis and entity recognition (NER) on large datasets to prepare labeled outputs for downstream machine learning training and validation. Collaborated with technical teams to define data quality frameworks, document annotation processes, and enable reproducible evaluation. • Sentiment analysis and NER labeling on large datasets • Text/data classification and labeling for ML training and validation • AI model output evaluation against criteria with feedback for refinement • Documentation of data quality procedures for reproducibility

2023 - Present

Education

Work History