Nyako B.

Key Skills

Software

Other

Telus

Scale AI

OneForma

Top Subject Matter

AI-generated multimedia content evaluation

AI response quality assurance and reviewer tasks

AI content quality annotation and dataset improvement

Top Data Types

Text

Image

Document

Top Task Types

Evaluation/Rating

Question Answering

Text Summarization

Object Detection

Segmentation

Data Collection

Freelancer Overview

AI Content Evaluator / AI Training Contributor (Outlier) (Remote, Project-Based). Core strengths include Other. Education includes Bachelor of Arts, University of Manchester (2022). AI-training focus includes data types such as Text and labeling workflows including Evaluation and Rating.

Labeling Experience

AI Content Evaluator / AI Training Contributor (Outlier) (Remote, Project-Based)

OtherText

Evaluated and ranked AI-generated content according to detailed project guidelines in an asynchronous remote workflow. Assessed instruction compliance and overall quality using provided training criteria. Worked independently to meet quality benchmarks and deadlines while contributing to AI model improvement. • Reviewed text and multimedia content outputs for guideline adherence • Applied consistent assessment standards across tasks • Identified content quality gaps affecting relevance and compliance • Submitted evaluated/ranked outputs to support downstream training

2024 - Present

AI Data Quality Reviewer (Tolus International AI)

OtherText

Reviewed AI-generated outputs from short-term AI projects for accuracy, relevance, and instruction compliance. Identified linguistic and quality-related errors across AI responses. Provided structured feedback to support AI model improvement and overall dataset quality. • Checked responses for factual and instruction alignment • Flagged linguistic errors and quality issues • Delivered structured feedback for remediation • Supported continuous data quality improvement

2020 - 2024

AI Content Quality Analyst (Scale AI)

OtherText

Assessed AI-generated content for clarity, consistency, and adherence to project guidelines in asynchronous remote work. Flagged quality issues that could degrade dataset usefulness. Supported dataset improvement by creating accurate annotations aligned with evaluation standards. • Evaluated clarity and consistency of AI outputs • Identified and flagged guideline adherence failures • Produced annotations to drive dataset corrections • Maintained required quality standards while working independently

2019 - 2019

Education

Work History