Samuel T.

Key Skills

Software

Don't disclose

Top Subject Matter

Search quality evaluation

local data quality assurance

digital content review

Top Data Types

Text

Image

Document

Top Task Types

Classification

Text Generation

Text Summarization

Data Collection

Transcription

Evaluation/Rating

Freelancer Overview

Data Analyst / Research Quality Reviewer, TELUS Digital (Remote). Brings 7+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Don't disclose. Education includes Bachelor of Arts, Southern New Hampshire University (2026) and Bachelor of Arts, Grand Valley State University (2025). AI-training focus includes data types such as Text and labeling workflows including Evaluation and Rating.

Labeling Experience

AI Data Evaluator / Quality Review Analyst, Handshake AI (Remote)

Don't discloseText

Evaluate AI-generated outputs across multimodal formats (text, image, video, audio, captions, and conversation-based tasks) using detailed rubrics and quality guidelines. Conduct head-to-head comparisons to determine which response better satisfies user intent, instruction-following, factual reliability, completeness, quality, safety, and usefulness. Review responses for unsupported claims, hallucinations, missing context, factual errors, inconsistency, poor reasoning, formatting problems, and guideline violations. • Rate and compare model outputs to support human preference outcomes. • Write concise, evidence-based justifications describing rating decisions and quality/safety concerns. • Provide structured human feedback to highlight performance gaps, safety concerns, and content quality patterns. • Support iterative model improvement via rubric-driven quality review.

2026 - Present

Data Analyst / Research Quality Reviewer, TELUS Digital (Remote)

Don't discloseText

Evaluate the accuracy, relevance, completeness, and quality of digital content and online search results against detailed project guidelines. Perform independent fact verification by comparing multiple sources and resolving inconsistencies to determine reliability, currency, and usefulness for end users. Identify missing details, conflicting information, low-quality results, and content errors before submitting evaluation decisions. • Assess search relevance, local information, news-related content, and map/local data quality. • Review geographic, cultural, business, and media context to improve search quality and downstream AI-assisted product performance. • Provide clear, guideline-aligned quality judgments and evidence-based decisions. • Focus on preventing factual errors, relevance issues, and low-quality submissions.

2026 - Present

Education

Work History