Hannah H.

Key Skills

Software

CVAT

CloudFactory

Data Annotation Tech

Remotasks

Top Subject Matter

Data pipeline quality control for analytical/annotation data

Healthcare AI systems and clinical data quality

Legal Services & Contract Review

Top Data Types

Text

Document

Image

Top Task Types

Text Generation

Text Summarization

Question Answering

Transcription

Evaluation/Rating

Prompt + Response Writing (SFT)

Data Collection

Freelancer Overview

Data Analyst Intern, data quality control and analytical output QA (TechStart Solutions, Romania Remote contract). Brings 1+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Internal and Proprietary Tooling. Education includes Data Analytics Program Certificate, Masai School.com (2026). AI-training focus includes data types such as Text, Medical, and DICOM and labeling workflows including Evaluation and Rating.

Labeling Experience

Research and Innovation AI System Scientist (S­taqu, health projects)

Worked as an AI system scientist on healthcare-focused projects developing tools for clinical infrastructure using LLMs and healthcare datasets. Contributed to shipping production-ready code used by real doctors and patients, requiring rigorous validation of outputs. Engaged in quality-focused AI system work that supports reliable dataset and model behavior in clinical contexts. • Built features for AI clinic platforms • Developed clinical infrastructure tools using LLMs • Worked with healthcare datasets • Supported production deployment and output validation

2026 - 2026

Data Analyst Intern, data quality control and analytical output QA (TechStart Solutions, Romania Remote contract)

Text

Conducted data quality checks on data pipelines by flagging inconsistencies and documenting corrections to maintain dataset hygiene. Collaborated with engineering and product teams to refine data collection specifications and ensure analytical outputs were accurate. Performed quality-control practices that are directly applicable to preparing labeled data for downstream AI training. • Flagged data inconsistencies during pipeline checks • Documented corrections and QA findings • Refined data collection specifications with stakeholders • Ensured accuracy of outputs for cross-functional use

2026 - 2026

Education

Work History