Lizel O.

Key Skills

Software

Don't disclose

Other

Remotasks

Top Subject Matter

Machine learning foundations

data annotation

conversational analytics

Top Data Types

Text

Document

Top Task Types

Entity (NER) Classification

Prompt + Response Writing (SFT)

Freelancer Overview

Data Annotation & Labelling Practicum | Employability BEST Program. Professional background includes roles such as Content Moderator and Customer Support & Front Desk Operator. Core strengths include Don't disclose, Other, and Remotasks. Education includes Data Annotation & Machine Learning Foundations Certificate, Employability BEST Program. AI-training focus includes data types such as Text and labeling workflows including Entity (NER) Classification, Prompt + Response Writing (SFT), and Evaluation.

Labeling Experience

Remote Task Specialist & QA Contractor | Remotasks

RemotasksText

Performed remote dataset analysis and labeling tasks using detailed step-by-step technical guides. Checked prompt-and-response data to ensure generated outputs were logical, accurate, and compliant with project requirements. Detected data anomalies and edge cases, flagged potential glitches, and supported quality improvements through careful review. • Followed complex annotation instructions to produce correct labels. • Spotted and resolved data anomalies and edge-case failures. • Reviewed prompt-response pairs for quality and requirement adherence. • Achieved consistent daily speed and accuracy while working independently from home.

2024 - Present

AI Evaluation & Model Alignment Project | Employability BEST Program

OtherTextPrompt + Response Writing (SFT)

Trained and evaluated LLM behavior by grading model outputs against structured rubrics for helpfulness, clarity, and safety. Produced high-quality prompt-and-response examples demonstrating accurate instruction-following for supervised fine-tuning data pipelines. Used research and fact-checking to assess technical claims and provided written feedback to explain answer quality issues. • Scored large language model responses using strict grading scales. • Wrote prompt-response pairs aligned to desired model behavior. • Conducted deep online research to verify technical or complex claims. • Delivered detailed feedback describing why responses were incorrect or lower quality.

2024

Data Annotation & Labelling Practicum | Employability BEST Program

Don't discloseTextEntity (NER) Classification

Completed supervised data annotation and machine learning foundations training to support clean dataset creation for downstream model performance. Labeled large collections of text for tasks such as entity extraction, intent detection, and text categorization under strict guidelines. Achieved over 95% classification accuracy by applying rubric-based decisions consistently. • Labeled unstructured conversational data by customer sentiment and emotional tone. • Followed annotation guidelines to maintain dataset quality. • Performed categorization and sorting of text samples for ML training. • Validated labels against quality expectations to improve consistency.

2024

Education

Work History