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Cheryl Pinas

Cheryl Pinas

Basic knowledge of AI/ML, skills in data annotation, var. industries

Netherlands flagAmsterdam, Netherlands
$20.00/hrEntry LevelOther

Key Skills

Software

Other

Top Subject Matter

No subject matter listed

Top Data Types

AudioAudio
DocumentDocument
VideoVideo

Top Task Types

Action Recognition
Question Answering
Text Summarization

Freelancer Overview

I am an information professional with a background in Information Services and Knowledge Management, complemented by solid experience in IT. My studies have given me a strong foundation in understanding how data is classified, managed, and applied across different domains. I have worked extensively with information systems and service delivery, which has sharpened my ability to structure, analyze, and process data effectively. With my company, Fusion Outsourcing, I am also able to scale when needed. In fact, it is my ambition to offer and manage this on a larger scale. This expertise allows me to contribute confidently to AI training projects, particularly in data labeling, annotation, and quality assurance. I understand the diverse forms data can take text, audio, images, and structured information and how consistent classification improves AI performance. Combined with my critical and ethical mindset, I bring both precision and reliability to AI training initiatives across industries.

Entry LevelDutchEnglishSpanish

Labeling Experience

Video editing and meta data labeling

OtherVideoSegmentation
The project involved preparing raw video material for an editor by performing video annotation and labeling tasks. The scope included segmenting long video files into meaningful clips, tagging scenes with descriptive labels (e.g., interview, event highlight, b-roll, transitions), and marking timestamps for key moments such as speaker changes, audience reactions, or important visual cues. This structured labeling allowed the video editor to quickly navigate the footage, identify relevant scenes, and streamline the editing workflow. Additional tasks included organizing metadata, applying quality checks to ensure timestamps were accurate, and categorizing clips according to the production storyboard. The project size encompassed several hours of raw footage per week, systematically labeled into dozens of usable clips. Quality measures included consistency in labeling according to predefined guidelines, cross-checking with the storyboard requirements, and periodic reviews with editors

The project involved preparing raw video material for an editor by performing video annotation and labeling tasks. The scope included segmenting long video files into meaningful clips, tagging scenes with descriptive labels (e.g., interview, event highlight, b-roll, transitions), and marking timestamps for key moments such as speaker changes, audience reactions, or important visual cues. This structured labeling allowed the video editor to quickly navigate the footage, identify relevant scenes, and streamline the editing workflow. Additional tasks included organizing metadata, applying quality checks to ensure timestamps were accurate, and categorizing clips according to the production storyboard. The project size encompassed several hours of raw footage per week, systematically labeled into dozens of usable clips. Quality measures included consistency in labeling according to predefined guidelines, cross-checking with the storyboard requirements, and periodic reviews with editors

2024 - 2025

Audio data labeling

OtherAudioSegmentation
The project involved audio data labeling to support video editing. Tasks included transcribing speech, annotating speakers and timestamps, and tagging key audio events such as applause, music cues, silence, and background noise. This structured labeling enabled editors to efficiently identify relevant segments, add subtitles, and optimize the overall sound quality of the final production.

The project involved audio data labeling to support video editing. Tasks included transcribing speech, annotating speakers and timestamps, and tagging key audio events such as applause, music cues, silence, and background noise. This structured labeling enabled editors to efficiently identify relevant segments, add subtitles, and optimize the overall sound quality of the final production.

2024

Sentiment Analysis

OtherTextText Summarization
The project focused on performing daily sentiment analysis on social media posts, online news articles, and media reports. The scope included the continuous monitoring of public opinion and narratives around political, economic, and social developments. The specific data labeling tasks performed involved classifying text data into categories such as positive, negative, neutral, and identifying recurring themes or entities (e.g., key persons, organizations, or topics). Each item was annotated with sentiment tags and, where relevant, enriched with contextual notes to capture underlying tones like irony, criticism, or support. The project covered thousands of data points per week, with a structured pipeline to process both high-volume social media streams and targeted media sources. To ensure reliability, we adhered to strict quality control measures: dual-annotation where needed, random sampling audits, and consistency checks using predefined guidelines.

The project focused on performing daily sentiment analysis on social media posts, online news articles, and media reports. The scope included the continuous monitoring of public opinion and narratives around political, economic, and social developments. The specific data labeling tasks performed involved classifying text data into categories such as positive, negative, neutral, and identifying recurring themes or entities (e.g., key persons, organizations, or topics). Each item was annotated with sentiment tags and, where relevant, enriched with contextual notes to capture underlying tones like irony, criticism, or support. The project covered thousands of data points per week, with a structured pipeline to process both high-volume social media streams and targeted media sources. To ensure reliability, we adhered to strict quality control measures: dual-annotation where needed, random sampling audits, and consistency checks using predefined guidelines.

2025 - 2025

Education

H

Hogeschool Utrecht

Post HBO Certificate, Pedagogical Didactic Certificate

Post HBO Certificate
2022 - 2024
T

The Hague University of Applied Science

Bachelor of Information and Communication Science, Information and Communication Science

Bachelor of Information and Communication Science
2001 - 2005

Work History

D

De Nationale Assemblee Suriname

ICT Manager

Paramaribo
2011 - Present
F

Freelance

Freelance Project/Process Manager

Amsterdam
2025 - Present