Vincent L.

Key Skills

Software

CVAT

Labelbox

Scale AI

Top Subject Matter

Computer Vision (NLP + CV tasks including object detection/semantic labeling)

NLP (Sentiment analysis, Named Entity Recognition)

Conversational AI (intent classification, entity tagging, response quality rating)

Top Data Types

Image

Text

Top Task Types

Segmentation

Bounding Box

Polygon

Classification

Object Detection

Entity (NER) Classification

Point/Key Point

Freelancer Overview

Data Annotation Specialist (DataCore AI Solutions) — Image segmentation and structured labeling. Core strengths include CVAT, Labelbox, and Scale AI. Education includes Bachelor of Science, University of Technology, Bordeaux (2022). AI-training focus includes data types such as Image and Text and labeling workflows including Segmentation, Entity (NER), and Evaluation.

Labeling Experience

Data Annotation Specialist

LabelboxImageBounding BoxPolygon

No description provided.

2023 - 2024

Data Annotation Specialist (DataCore AI Solutions) — Text labeling for NLP tasks

LabelboxText

Annotated text data for NLP model training including named entity recognition and sentiment-related tasks. Applied guideline-driven labeling and escalated ambiguous cases to project leads to improve ontology clarity. Ensured labeled outputs met QA and throughput requirements for production pipelines. • Labeled text for NER and sentiment analysis related annotation objectives • Interpreted complex annotation guidelines and ontologies for consistent outputs • Flagged ambiguous cases to project leads and contributed to guideline refinement • Met daily throughput targets while maintaining QA accuracy benchmarks

2023 - 2024

Data Annotation Specialist (DataCore AI Solutions) — Image segmentation and structured labeling

CVATImageSegmentation

Labeled and annotated image data for NLP and computer vision training projects using bounding boxes and polygon/semantic segmentation. Followed complex guidelines and ontologies while flagging ambiguous cases for refinement. Maintained high accuracy through QA processes and peer review. • Applied bounding boxes and polygon segmentation for consistent dataset quality • Completed semantic labeling to support computer vision model training • Achieved inter-annotator agreement (IAA) score above 95% on annotated sets • Conducted quality checks on peer work to correct edge cases and reduce error rates by ~12%

2023 - 2024

Junior Data Labeler

LabelboxImageBounding BoxPolygon

No description provided.

2022 - 2022

Junior Data Labeler (Freelance / Appen & Scale AI) — Conversational AI data annotation and ratings

Scale AIText

Performed text and conversational data labeling for crowd-sourced AI training tasks across multiple clients. Labeled conversational content with dialogue acts, entity tags, and assessed response quality against task criteria. Maintained a 98%+ quality score while completing intent, classification, and relevance rating work. • Completed text classification, intent labeling, and relevance rating tasks • Labeled dialogue acts and entity tagging for conversational AI training data • Rated response quality to support evaluation and model improvement • Sustained 98%+ quality and earned access to higher-tier specialized projects

2022 - 2022

Education

Work History