Dammy Ogunmoye

Key Skills

Software

Mercor

Top Subject Matter

Technical content

Prompts Domain Expertise

engineering workflows

Top Data Types

Text

Computer Code Programming

Document

Top Task Types

Classification

Freelancer Overview

Data Labeler / AI Data Specialist. Brings 11+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Outlier, Mercor, and Handshake. Education includes Bachelor of Computer Science, Lead City University (2012). AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and Classification.

Labeling Experience

Data Labeler

TextClassification

At Handshake, contributed to structured data labeling and review, focusing on platform-based technical content workflows. Responsible for tagging, categorizing, validating, and refining records according to strict annotation guidelines. Maintained strong productivity while ensuring audit-readiness and detail orientation in a feedback-driven environment. • Collaborated closely with other reviewers for quality alignment. • Ensured annotated records were consistently categorized for downstream use. • Maintained documentation and process compliance. • Adapted to reviewer feedback and rapidly changing guidelines.

At Handshake, contributed to structured data labeling and review, focusing on platform-based technical content workflows. Responsible for tagging, categorizing, validating, and refining records according to strict annotation guidelines. Maintained strong productivity while ensuring audit-readiness and detail orientation in a feedback-driven environment. • Collaborated closely with other reviewers for quality alignment. • Ensured annotated records were consistently categorized for downstream use. • Maintained documentation and process compliance. • Adapted to reviewer feedback and rapidly changing guidelines.

Present

Data Labeler / AI Evaluation Specialist

MercorText

At Mercor, conducted data labeling and evaluation with a focus on technical and engineering-oriented tasks. Evaluated content for correctness, relevance, completeness, and instruction-following as per project standards. Supported high-volume, quality-focused workflows while managing annotation conflicts and edge cases. • Flagged low-confidence and ambiguous items for quality review. • Maintained compliance with dynamic project-specific standards. • Managed workload efficiently to preserve accuracy. • Focused on engineering-style annotation over data modeling.

At Mercor, conducted data labeling and evaluation with a focus on technical and engineering-oriented tasks. Evaluated content for correctness, relevance, completeness, and instruction-following as per project standards. Supported high-volume, quality-focused workflows while managing annotation conflicts and edge cases. • Flagged low-confidence and ambiguous items for quality review. • Maintained compliance with dynamic project-specific standards. • Managed workload efficiently to preserve accuracy. • Focused on engineering-style annotation over data modeling.

Present

Data Labeler / AI Data Specialist

Text

At Outlier, performed engineering-focused data labeling and review of technical prompts, responses, and associated evaluation criteria. Tasks included classifying, ranking, validating, and improving outputs by closely following detailed project instructions. Maintained annotation quality and consistency, documenting ambiguous cases and adapting to evolving guidelines. • Annotated and evaluated code-related technical content. • Ensured adherence to structured rubrics and escalation rules. • Identified and documented exceptions and edge cases. • Preserved high-quality outputs during guideline changes.

At Outlier, performed engineering-focused data labeling and review of technical prompts, responses, and associated evaluation criteria. Tasks included classifying, ranking, validating, and improving outputs by closely following detailed project instructions. Maintained annotation quality and consistency, documenting ambiguous cases and adapting to evolving guidelines. • Annotated and evaluated code-related technical content. • Ensured adherence to structured rubrics and escalation rules. • Identified and documented exceptions and edge cases. • Preserved high-quality outputs during guideline changes.

Present

Education

Work History