Victor J.

Key Skills

Software

Anno-Mage

Axiom AI

Data Annotation Tech

Doccano

Top Subject Matter

Healthcare

computer science

Banking

Top Data Types

Video

Audio

Document

Top Task Types

Bounding Box

Polygon

Segmentation

Transcription

Data Collection

Computer Programming/Coding

Freelancer Overview

I have extensive experience in data labeling and AI training data preparation, with a strong focus on ensuring accuracy, consistency, and scalability in large datasets. My background includes designing annotation guidelines, managing labeling workflows, and implementing quality assurance processes that improve model performance. I’ve worked on diverse projects ranging from natural language processing (NLP) tasks like sentiment analysis and named entity recognition to computer vision datasets involving object detection and image classification. This breadth of exposure has given me a deep understanding of how well-structured training data directly impacts the reliability of AI systems. What sets me apart is my ability to bridge the gap between technical requirements and human annotation processes. I’ve led projects where I optimized labeling pipelines by introducing automation tools, reducing error rates, and accelerating turnaround times. Additionally, I bring expertise in evaluating dataset bias, curating balanced samples, and applying domain-specific knowledge to enhance data relevance. My combination of technical rigor, project management skills, and attention to detail ensures that the training data I produce not only meets immediate project needs but also supports long-term scalability and fairness in AI models.

Labeling Experience

data antonnator

VideoBounding Box

One of the largest projects I contributed to involved building a multilingual dataset for a natural language processing model designed to handle customer support queries. The scope included text classification, intent recognition, and named entity labeling across five languages. My role was to design annotation guidelines, train annotators, and perform hands-on labeling for complex cases. The project size was substantial—over 1.5 million text samples—and required balancing speed with precision. I also coordinated with engineers to integrate semi-automated labeling tools, which reduced repetitive tasks and allowed human reviewers to focus on edge cases. Quality assurance was central to the project. We implemented a multi-tier review system where each batch of labeled data underwent peer review, random sampling audits, and statistical consistency checks. Inter-annotator agreement (IAA) was tracked continuously, with thresholds set to flag discrepancies early. In addition, bias detection measures were applied to ensure balanced representation across demographics and languages. These quality measures not only improved the dataset’s reliability but also directly enhanced the downstream model’s accuracy and fairness. This combination of scale, rigor, and process optimization is what made the project stand out

2020 - 2024

Education

Work History