Vincent Dave Peñaranda

Key Skills

Software

Appen

Lionbridge

Remotasks

Other

Scale AI

Internal/Proprietary Tooling

Top Subject Matter

No subject matter listed

Top Data Types

3D Sensor

Image

Text

Video

Top Task Types

Bounding Box

Classification

Cuboid

Data Collection

Entity Ner Classification

Evaluation Rating

Polygon

Freelancer Overview

I am an experienced data analyst and remote worker with a strong background in data analytics, customer success, and technical writing, including experience supporting digital health and e-commerce projects. My expertise spans data annotation, data-driven strategies, and data management using tools such as Microsoft 365, Google Workspace, IBM SPSS, Stata, Anaconda, and project management platforms like Notion, Trello, and Asana. I have hands-on experience working with AI tools including ChatGPT, Jasper AI, and Claude, and am skilled at ensuring high-quality, accurate data labeling for analytics and AI training purposes. My ability to collaborate across teams, manage complex datasets, and communicate insights effectively allows me to contribute to projects in domains such as healthcare, sales, and digital marketing. I am committed to delivering reliable training data and supporting AI model development through meticulous annotation and quality assurance.

Labeling Experience

Appen Worker

AppenTextClassificationQuestion Answering

A Search Engine Result Evaluator at Appen (often associated with projects like Yukon or Wells) is a highly analytical role focused on improving search algorithm accuracy by providing human feedback on the relevance and quality of web results. The project scope involves analyzing specific search queries and rating the returned results—including websites, maps, and advertisements—based on strict "Needs Met" (relevance to user intent) and "Page Quality" (trustworthiness and expertise) scales. Evaluators perform detailed tasks such as assessing query intent, checking the factual accuracy of content, and ensuring that high-authority sources are prioritized for "Your Money or Your Life" (YMYL) topics. The project size is extensive, operating across hundreds of languages and regions with thousands of contractors, requiring a weekly commitment that typically ranges from 10 to 20 hours. Quality is maintained through rigorous measures including a multi-part qualification exam.

A Search Engine Result Evaluator at Appen (often associated with projects like Yukon or Wells) is a highly analytical role focused on improving search algorithm accuracy by providing human feedback on the relevance and quality of web results. The project scope involves analyzing specific search queries and rating the returned results—including websites, maps, and advertisements—based on strict "Needs Met" (relevance to user intent) and "Page Quality" (trustworthiness and expertise) scales. Evaluators perform detailed tasks such as assessing query intent, checking the factual accuracy of content, and ensuring that high-authority sources are prioritized for "Your Money or Your Life" (YMYL) topics. The project size is extensive, operating across hundreds of languages and regions with thousands of contractors, requiring a weekly commitment that typically ranges from 10 to 20 hours. Quality is maintained through rigorous measures including a multi-part qualification exam.

2021 - 2022

Lidar Bee Tasker

Scale AIGeospatial Tiled ImageryBounding BoxEntity Ner Classification

The Lidar Bee project (specifically Bee LSS) is a high-complexity, 3D LiDAR segmentation and annotation project designed to train autonomous vehicle AI by reconstructing driving environments through millions of laser-scanned points. Taskers are responsible for precise 3D cuboid fitting (adjusting length, width, height, heading, pitch, and roll), semantic segmentation (coloring individual points by category), and object tracking across multi-frame sequences to ensure temporal consistency. Given its intensive scale—often requiring high-performance hardware and several hours per task—the project adheres to rigorous quality measures including a 95–98% accuracy threshold, mandatory "Bootcamp" training, automated collision/movement lints, and a multi-tier review process where senior auditors must approve work before final payment is released.

The Lidar Bee project (specifically Bee LSS) is a high-complexity, 3D LiDAR segmentation and annotation project designed to train autonomous vehicle AI by reconstructing driving environments through millions of laser-scanned points. Taskers are responsible for precise 3D cuboid fitting (adjusting length, width, height, heading, pitch, and roll), semantic segmentation (coloring individual points by category), and object tracking across multi-frame sequences to ensure temporal consistency. Given its intensive scale—often requiring high-performance hardware and several hours per task—the project adheres to rigorous quality measures including a 95–98% accuracy threshold, mandatory "Bootcamp" training, automated collision/movement lints, and a multi-tier review process where senior auditors must approve work before final payment is released.

2021 - 2022

EN-data contributor at Google TeraNeuro Project

Internal Proprietary ToolingTextEntity Ner ClassificationRelationship

As a Google TeraNeuro English Data Contributor, my role centers on the project's mission to refine high-scale AI through precise linguistic curation. My scope involves the dataset's transformation from raw text into "Gold Standard" material by executing the analyst's specific labeling tasks, such as fact-checking's rigorous verification and the annotator's semantic tagging. Given the initiative's "Tera" size, my output must meet the project's elite quality measures, which rely on the reviewer's audit and the system's automated accuracy checks. This ensures the model's training data adheres strictly to a company's safety standards and the user's need for factual integrity.

As a Google TeraNeuro English Data Contributor, my role centers on the project's mission to refine high-scale AI through precise linguistic curation. My scope involves the dataset's transformation from raw text into "Gold Standard" material by executing the analyst's specific labeling tasks, such as fact-checking's rigorous verification and the annotator's semantic tagging. Given the initiative's "Tera" size, my output must meet the project's elite quality measures, which rely on the reviewer's audit and the system's automated accuracy checks. This ensures the model's training data adheres strictly to a company's safety standards and the user's need for factual integrity.

2017 - 2019

Education

Work History