Furutan S.

Key Skills

Software

Other

Top Subject Matter

AI output evaluation and text annotation for NLP systems

Data labeling and classification ML model training

Scientific data documentation and quality review

Top Data Types

Text

Document

Top Task Types

Classification

Data Collection

Freelancer Overview

Detail-oriented Chemistry graduate and Data Science fellow with hands-on experience in AI output evaluation, data annotation, and structured quality review. Practiced at labeling and assessing AI-generated content for accuracy, relevance, and task-fitness across multiple platforms. Completed MIT Open Learning's Introduction to Universal AI (May 2026) and ranked Top 100 out of ~1,000 fellows in the ST@40 national data internship. Strong critical reading, guideline adherence, and independent work ethic developed across scientific, academic, and digital contexts. Native English speaker available immediately for remote labeling tasks.

Labeling Experience

Data Science Fellow — 3MTT NextGen Cohort (Federal Ministry of Communications & Digital Economy)

OtherText

Practiced data labeling and ML output evaluation as part of a national data science fellowship cohort. Worked on classification and regression dataset labeling using Python tools to prepare inputs for model training. Reviewed model predictions and annotated misclassified samples to support retraining and improved performance. • Studied data annotation pipelines, NLP fundamentals, and ML model evaluation concepts. • Labeled datasets for classification and regression tasks using Python (Pandas, NumPy, scikit-learn). • Evaluated ML outputs for correctness and annotated errors for retraining. • Performed iterative review to improve dataset quality for ML workflows.

2026 - Present

AI Output Annotator — DLAP & ContentForge AI Platforms

Text

Evaluated AI-generated text for correctness, relevance, coherence, and task-fit across platform development and testing. Applied consistent judgment criteria to classify responses and identify issues such as hallucinations and low-quality outputs. Provided structured written feedback to support iterative improvement during annotation review. • Labeled outputs as correct, incorrect, or needing improvement across hundreds of test cases. • Flagged errors and hallucinations in Claude API responses with detailed notes. • Followed annotation guidelines independently with no external supervision. • Maintained consistency across reviewed AI responses.

2024 - Present

Data Analyst & Labeling Intern — ST@40 Program (Schull Technologies)

TextClassification

Cleaned, categorized, and labeled a real-world Titanic dataset to support supervised machine learning training. Classified passenger features and annotated data patterns for downstream model development and evaluation. Visualized labeled outputs and validated annotation accuracy using dashboard reporting. • Handled missing values, outliers, and inconsistent entries during data labeling. • Annotated passenger feature patterns for supervised learning model training. • Built a Power BI dashboard to validate labeled data output quality. • Ranked Top 100 among ~1,000 fellows in a competitive internship program.

2025 - 2026

Subject Teacher & Laboratory Supervisor — NYSC (Promiseland High School)

Don't discloseDocumentClassification

Maintained structured records and documentation across student laboratory activities following strict reporting guidelines. Categorized and logged laboratory test results and observations to ensure consistent, high-quality labeled documentation. Worked independently to meet all documentation deadlines without supervision. • Documented and organized lab records for 15+ students with precision. • Categorized and logged laboratory test results using structured guidelines. • Ensured consistent labeling and documentation practices independently. • Met all reporting deadlines without supervision.

2024 - 2025

Laboratory Assistant (ITF) — Ta’al Laboratory, Lafia

Don't discloseDocumentData Collection

Recorded, categorized, and verified laboratory test data to ensure high-accuracy documentation. Produced structured test records that reflect quality control practices relevant to reliable data labeling standards. Focused on preventing errors through verification and consistent categorization procedures. • Recorded and categorized laboratory test data with high accuracy. • Verified entries to maintain zero-error precision in documentation. • Applied consistent structured data handling for reporting. • Supported data quality through systematic checks.

2021 - 2022

Education

Work History