Fatima Hassan

Key Skills

Software

Scale AI

Top Subject Matter

Medical (Oncology, Radiotherapy, Clinical Information) and General Knowledge

Legal Services & Contract Review

Regulatory Compliance & Risk Analysis

Top Data Types

Text

Document

Medical Dicom

Top Task Types

No task types listed

Freelancer Overview

AI Data Annotator & Evaluator (Independent / Professional Development). Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Scale AI. Education includes Bachelor of Science, University of Hertfordshire (2021) and A Level, Southfields Academy (2018). AI-training focus includes data types such as Text and labeling workflows including Evaluation and Rating.

Labeling Experience

AI Data Annotator & Evaluator (Independent / Professional Development)

Scale AIText

Served as an AI Data Annotator & Evaluator with hands-on responsibility for evaluating and comparing pairs of AI-generated chatbot responses using structured criteria. Performed systematic fact-checking of AI outputs across both medical and general knowledge domains to identify factual errors and ensure quality. Designed and authored prompts, created evaluation rubrics, and applied expertise in the medical field to annotate health-related AI outputs.  Conducted structured preference labeling following RLHF methodologies.  Fact-checked AI-generated content, flagging inaccuracies.  Scored responses on coherence, completeness, safety, and relevance using rubrics.  Created and assessed prompts and bespoke evaluation rubrics across various domains.

Served as an AI Data Annotator & Evaluator with hands-on responsibility for evaluating and comparing pairs of AI-generated chatbot responses using structured criteria. Performed systematic fact-checking of AI outputs across both medical and general knowledge domains to identify factual errors and ensure quality. Designed and authored prompts, created evaluation rubrics, and applied expertise in the medical field to annotate health-related AI outputs.  Conducted structured preference labeling following RLHF methodologies.  Fact-checked AI-generated content, flagging inaccuracies.  Scored responses on coherence, completeness, safety, and relevance using rubrics.  Created and assessed prompts and bespoke evaluation rubrics across various domains.

2024 - Present

Education

Work History