Christopher Paillet

Key Skills

Software

CloudFactory

Top Subject Matter

No subject matter listed

Top Data Types

3D Sensor

Top Label Types

Polygon

Freelancer Overview

I am a seasoned data scientist and AI training specialist with over a decade of experience designing, curating, and optimizing high-quality datasets to power machine learning and large language models. My expertise spans the full data lifecycle—from building robust data labeling and annotation pipelines to establishing rigorous quality control and human-in-the-loop workflows that ensure training data reliability, alignment, and fairness. I have led efforts in dataset curation, statistical validation, and bias mitigation, with a strong focus on NLP and text classification projects. My technical toolkit includes Python, SQL, R, PyTorch, TensorFlow, Scikit-learn, and cloud-based data pipelines on AWS and GCP. I am passionate about developing ethical, scalable data solutions that drive real-world AI performance and safety, and I thrive in collaborative environments where I can help teams deliver high-impact, trustworthy AI systems.

Labeling Experience

Data Annotation

Cloudfactory3D SensorPolygon

Worked on a large-scale 3D sensor data annotation project focused on high-precision labeling for AI and machine learning model training. The project involved annotating complex sensor-generated datasets using polygon-based labeling techniques to accurately define object boundaries and spatial relationships. Responsibilities included reviewing and annotating 3D data outputs, ensuring precise polygon placement, and maintaining consistency across large volumes of data. Followed strict annotation guidelines and quality standards to support downstream model performance, particularly in outlier detection and edge-case scenarios. Collaborated within a structured labeling workflow using CloudFactory, adhering to multi-level quality assurance processes including self-review, peer review, and auditor feedback. Emphasized accuracy, completeness, and consistency to reduce label noise and improve training data reliability. Contributed to improving dataset quality by identifying ambiguous cases,

Worked on a large-scale 3D sensor data annotation project focused on high-precision labeling for AI and machine learning model training. The project involved annotating complex sensor-generated datasets using polygon-based labeling techniques to accurately define object boundaries and spatial relationships. Responsibilities included reviewing and annotating 3D data outputs, ensuring precise polygon placement, and maintaining consistency across large volumes of data. Followed strict annotation guidelines and quality standards to support downstream model performance, particularly in outlier detection and edge-case scenarios. Collaborated within a structured labeling workflow using CloudFactory, adhering to multi-level quality assurance processes including self-review, peer review, and auditor feedback. Emphasized accuracy, completeness, and consistency to reduce label noise and improve training data reliability. Contributed to improving dataset quality by identifying ambiguous cases,

2020 - 2023

Education

Work History