POVINELLI ROBERT
povinelirobert@gmail.com • Virginia/Ashburn
DATA ANNOTATOR
PROFESSIONAL SUMMARY
Accuracy-driven Data Annotator with expertise in labeling and categorizing text, image, and audio datasets for machine
learning. Skilled in following precise annotation guidelines to ensure 100%+ accuracy and consistency for AI/ML training.
Collaborative team player experienced in working with data scientists to refine labeling processes and improve model
performance.
PROFESSIONAL EXPERIENCE
Appen ai, Remote January. 2025 - March. 2025
Data annotator
• Accurately labeled and categorized 1,000+ text/image/audio datasets daily for AI/ML training, maintaining 99%+
consistency per project guidelines.
• Specialized in NLP annotations (entity recognition, sentiment analysis) and computer vision tasks (object
detection, bounding boxes) to enhance model accuracy.
• Collaborated with QA teams to identify and correct edge cases, improving dataset reliability for client AI models.
• Met aggressive deadlines while processing high-volume data batches, contributing to on-time project deliveries.
• Adapted quickly to evolving annotation protocols across 5+ projects (e.g., search engine algorithms, voice assistant
training).
Data Annotation Tech, Remote March. 2024 - September. 2024
Data annotator specialist
• Annotated 10,000+ complex datasets (text, images, LiDAR) for AI/ML training with >100% accuracy, adhering
to strict labeling guidelines for autonomous vehicle and NLP models.
• Led quality control initiatives, identifying and resolving 200+ inconsistent labels weekly, improving dataset
reliability by 30% for client AI deployments.
• Trained and mentored 5 new annotators on tooling (CVAT, Label Studio) and domain-specific protocols
(medical imaging, geospatial tagging).
• Documented 15+ edge cases (e.g., occluded objects, ambiguous text) and collaborated with data engineers to refine
annotation frameworks.
• Reduced processing time 25% by optimizing workflow shortcuts in Prodigy Annotation Tool without sacrificing
quality.
Scale AI, San Francisco, CA (HQ) May. 2023 – January. 2024
Quality Control Analyst
• Audited 1,000+ labeled frames/day for autonomous vehicle datasets, flagging 200+ inconsistencies/week to
maintain >99% dataset integrity.
• Spearheaded a calibration initiative for LiDAR annotation tools, reducing sensor misalignment errors by 42%.
• Authored QC playbooks adopted company-wide, standardizing evaluations for 5+ project types (3D object
detection, lane marking, and traffic signs).
• Presented weekly metrics (error rates, throughput) to leadership, influencing tooling upgrades that cut processing
time by 25%.
Scale AI, San Francisco, CA (HQ) January. 2023 – April. 2023
Internship
• Assisted researchers to determine and understand input data and annotated by drawing boxes to highlight areas of
interest in images.
• Supported researchers in training deep learning models through accurately categorizing content, labelling images,
and annotating data.
• Reported and documented issues faced from data annotation to the research team and discuss solutions to improve
data
EDUCATION
BSC in Computer Science at University of California, Berkeley | 2022
CERTIFICATES
Certified Data Management Professional (CDMP)
Data Management Association International (DAMA)
SKILLS & OTHER
Annotation Expertise | Labelbox | Ground Truth | Domain Knowledge | Quality Assurance | Anomaly Detection | Semantic
Understanding | Image Processing Techniques | Machine Learning Concepts | Analytical Skills | Problem-Solving Skills |
Data Management | Attention to Detail | Python
Labelbox (Expert)
LANGUAGES
English
Data Annotation Software (Expert) Snorkel (Advanced)
Data Management (Advanced) English Language Skills (Expert)
Python (Intermediate) Verbal And Written Communication (Expert)
Machine Learning Basics (Intermediate) Verbal And Written Communication (Expert)
Analytical Skills (Expert) Verbal And Written Communication (Expert)
Problem-Solving Skills (Expert)
