Akinyi Byron Ochieng

Key Skills

Software

Remotasks

Appen

Labelbox

Scale AI

Top Subject Matter

Natural Language Processing (NLP) & Text Annotation

Computer Vision & Image/Video Labeling

RLHF & Conversational AI Evaluation

Top Data Types

Text

Image

Document

Top Label Types

Entity Ner Classification

Classification

Freelancer Overview

Senior Data Annotation Specialist & AI Trainer. Brings 7+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Scale AI and Appen. Education includes Bachelor of Science, University of Nairobi (2019). AI-training focus includes data types such as Text and labeling workflows including Entity (NER) Classification and Classification.

Labeling Experience

Senior Data Annotation Specialist & AI Trainer

Scale AITextEntity Ner Classification

I annotated and labeled over 60,000 text and image data points for NLP, computer vision, and multimodal AI datasets, ensuring high accuracy verified by blind quality-check audits. My tasks included Named Entity Recognition (NER) tagging, intent classification, sentiment labeling, and text span annotation across diverse domains. I applied structured rubric-based evaluations, contributed to RLHF training, and promoted best practices in annotation reliability. • Maintained a 98.6% annotation accuracy rate and achieved a 96.8% inter-rater agreement (IRA) score • Used platforms including Outlier AI, Remotasks (Scale AI), CVAT, and Labelbox for annotation and review • Promoted to peer-review auditor and audited over 1,200 weekly annotation submissions • Identified and escalated edge cases, helping revise annotation guidelines for improved model precision

I annotated and labeled over 60,000 text and image data points for NLP, computer vision, and multimodal AI datasets, ensuring high accuracy verified by blind quality-check audits. My tasks included Named Entity Recognition (NER) tagging, intent classification, sentiment labeling, and text span annotation across diverse domains. I applied structured rubric-based evaluations, contributed to RLHF training, and promoted best practices in annotation reliability. • Maintained a 98.6% annotation accuracy rate and achieved a 96.8% inter-rater agreement (IRA) score • Used platforms including Outlier AI, Remotasks (Scale AI), CVAT, and Labelbox for annotation and review • Promoted to peer-review auditor and audited over 1,200 weekly annotation submissions • Identified and escalated edge cases, helping revise annotation guidelines for improved model precision

2023 - 2023

Data Labeling & Content Rating Analyst

AppenTextClassification

I conducted high-volume content rating and relevance annotation tasks spanning web search, image, and conversational AI datasets, completing over 150,000 annotations. My responsibilities included evaluating AI-generated conversational outputs for safety and quality and applying fine-grained ontologies and taxonomies for content categorization. I produced formal memos on guideline ambiguities that directly influenced annotation standards. • Maintained a high accuracy average of 97.9% across projects and qualified for Appen's specialist task pipeline • Applied structured rubrics for dialogue safety, tone, and quality on more than 12,000 multi-turn conversation pairs • Demonstrated rigorous compliance with guidelines and participated in ongoing annotation quality improvements • Categorized content for implicit bias detection, toxicity scoring, and nuanced intent identification

I conducted high-volume content rating and relevance annotation tasks spanning web search, image, and conversational AI datasets, completing over 150,000 annotations. My responsibilities included evaluating AI-generated conversational outputs for safety and quality and applying fine-grained ontologies and taxonomies for content categorization. I produced formal memos on guideline ambiguities that directly influenced annotation standards. • Maintained a high accuracy average of 97.9% across projects and qualified for Appen's specialist task pipeline • Applied structured rubrics for dialogue safety, tone, and quality on more than 12,000 multi-turn conversation pairs • Demonstrated rigorous compliance with guidelines and participated in ongoing annotation quality improvements • Categorized content for implicit bias detection, toxicity scoring, and nuanced intent identification

2022 - 2023

Education

Work History