Antony W.

Key Skills

Software

Appen

Argilla

Axiom AI

Clickworker

CloudFactory

Data Annotation Tech

Datatroniq

Labelbox

OneForma

Scale AI

Label Studio

AWS SageMaker

Other

Top Subject Matter

Recommendation engines

churn prediction

and customer-related text analytics

Top Data Types

3D Sensor

Audio

Document

Text

Image

Top Task Types

Audio Recording

Bounding Box

Computer Programming/Coding

Data Collection

Prompt + Response Writing (SFT)

Classification

Emotion Recognition

Segmentation

Polygon

Object Detection

RLHF

Text Summarization

Transcription

Text Generation

Evaluation/Rating

Red Teaming

Fine-tuning

Function Calling

Point/Key Point

Polyline

Cuboid

Question Answering

Entity (NER) Classification

Freelancer Overview

Data Scientist (Labeling & QA of training datasets) at Google (2018 to Present). Brings 12+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Labelbox, Label Studio, and AWS SageMaker. Education includes Master of Science, New York University (2014) and Bachelor of Science, Stanford University (2012). AI-training focus includes data types such as Text and Document and labeling workflows including Classification, Entity (NER), and Emotion Recognition.

Labeling Experience

Data Scientist (Labeling & QA of training datasets) at Google (2018 to Present)

LabelboxTextClassification

You collaborated with ML teams to label and QA large-scale text datasets used for recommendation and churn prediction. You defined annotation guidelines and managed vendor labeling pipelines through Labelbox to ensure consistent dataset quality. You integrated active learning feedback loops to connect model predictions with labeling priorities and improve iterative labeling efficiency. • Defined labeling guidelines and taxonomy requirements with data ops teams • Performed QA and inter-annotator quality checks for labeled datasets • Coordinated vendor labeling workflow execution in Labelbox • Used active learning to reprioritize data for annotation based on model uncertainty.

2018 - Present

Sentiment Analysis of Customer Reviews (custom labeled corpus)

OtherTextEmotion Recognition

You created and labeled a custom corpus for sentiment classification of customer reviews. You applied NLP techniques alongside annotation guidelines to improve model precision on unseen feedback. You used the labeled dataset to drive insights for customer experience optimization. • Labeled review text into sentiment categories (positive/neutral/negative) • Established annotation guidelines to standardize labeling • Applied NLP-based preprocessing and modeling support • Enabled model evaluation and iterative improvement using labeled data.

2019 - 2020

Predictive Maintenance for Industrial Equipment (labeled dataset creation & QA)

AWS SageMakerDocumentClassification

You built and maintained labeled datasets for fault detection to support predictive maintenance workflows. You defined an annotation schema for labeled fault events and managed labeling QA to ensure reliable model training inputs. You used Snowflake and AWS ML pipelines to operationalize the labeled dataset for proactive maintenance forecasting. • Defined fault detection annotation schema and label structure • Managed labeling QA processes to maintain label quality • Prepared labeled data for downstream ML training pipelines • Supported proactive maintenance model development using labeled fault data.

2018 - 2020

Junior Data Scientist (Annotation & labeling support) at IBM (2015 to 2018)

Label StudioText

You supported annotation and labeling of training datasets for demand forecasting and A/B testing models. You designed taxonomies and QA protocols for labeled campaign data to improve labeled data consistency and downstream performance. You conducted manual and automated text classification as part of NLP sentiment analysis labeling workflows. • Built labeling taxonomies and QA protocols for campaign datasets • Performed manual and automated text classification for sentiment analysis • Assisted in preparing labeled data for demand forecasting and experiments • Supported end-to-end annotation workflow execution and validation.

2015 - 2018

Education

Work History