Praise Sunday

Key Skills

Software

CVAT

Other

Top Subject Matter

Computer Vision & Robotics

Process Automation & Analytics

Analytics & Data Science

Top Data Types

Video

Text

Image

Document

Top Task Types

Action Recognition

Classification

Object Detection

Freelancer Overview

I have hands-on experience annotating and correcting video datasets, including robotic action sequences, producing precise timestamped action labels, identifying left/right limb usage, and classifying task outcomes as successful, partial, or failed. I am proficient in CVAT, Label Studio, Roboflow, and Supervisely, with specific experience reviewing and refining model-generated annotations to meet gold-standard quality benchmarks. Backed by 6+ years of Python and SQL expertise. What distinguishes me is the combination of annotation accuracy and the ability to sustain it at volume. I have produced 45+ enterprise-grade structured outputs under strict consistency standards, regularly corrected automated system outputs for misclassifications and errors, and decomposed complex multi-step processes into precise, timestamped sequences for compliance and audit purposes, all skills that transfer directly and immediately to this role.

Labeling Experience

AI Data Analyst & Video Annotation Specialist

CVATVideoAction Recognition

Annotated, reviewed, and corrected structured video data outputs for AI and machine learning pipelines. Labeled robotic action sequences, decomposed multi-stage tasks into timestamped action steps, and classified task outcomes using structured taxonomies. Utilized industry-standard annotation platforms (CVAT, Label Studio, Roboflow) to maintain high-quality, guideline-compliant outputs. • Broke down complex video sequences into discrete actions with precise timestamps. • Classified robotic tasks as successful, partial, or failed with detailed documentation. • Reviewed, validated, and corrected AI model-generated video annotations for accuracy and consistency. • Applied annotation guidelines strictly with iterative feedback cycles to achieve dataset benchmark standards.

Annotated, reviewed, and corrected structured video data outputs for AI and machine learning pipelines. Labeled robotic action sequences, decomposed multi-stage tasks into timestamped action steps, and classified task outcomes using structured taxonomies. Utilized industry-standard annotation platforms (CVAT, Label Studio, Roboflow) to maintain high-quality, guideline-compliant outputs. • Broke down complex video sequences into discrete actions with precise timestamps. • Classified robotic tasks as successful, partial, or failed with detailed documentation. • Reviewed, validated, and corrected AI model-generated video annotations for accuracy and consistency. • Applied annotation guidelines strictly with iterative feedback cycles to achieve dataset benchmark standards.

2022 - 2025

Data Annotation & QA Analyst (Contract)

OtherTextClassification

Reviewed, validated, and corrected structured textual data records and automated outputs for data quality standards. Identified and documented misclassifications, labeling errors, and process anomalies for use in AI and machine learning datasets. Operated according to strict annotation and data formatting guidelines to ensure quality and traceability. • Corrected labeled records across multiple data environments for accuracy. • Diagnosed and reported process failures, mislabeling, and outcome errors with structured descriptions. • Provided reliable, reproducible annotation pipeline contributions for AI applications. • Ensured team outputs met guideline and reproducibility standards for large projects.

Reviewed, validated, and corrected structured textual data records and automated outputs for data quality standards. Identified and documented misclassifications, labeling errors, and process anomalies for use in AI and machine learning datasets. Operated according to strict annotation and data formatting guidelines to ensure quality and traceability. • Corrected labeled records across multiple data environments for accuracy. • Diagnosed and reported process failures, mislabeling, and outcome errors with structured descriptions. • Provided reliable, reproducible annotation pipeline contributions for AI applications. • Ensured team outputs met guideline and reproducibility standards for large projects.

2021 - 2022

Business Intelligence & Data Annotation Analyst

OtherTextClassification

Documented, validated, and corrected outputs from automated analytics and reporting pipelines, ensuring annotation accuracy before final reporting. Contributed to the establishment of annotation and documentation standards across analytical workflows. Collaborated cross-functionally to resolve annotation ambiguities and align output quality for AI and data science initiatives. • Reviewed and corrected process outputs for accuracy and guideline compliance. • Created human-readable narratives describing process workflows step-by-step. • Coordinated team-wide adoption of annotation and quality documentation practices. • Aligned annotation deliverables with project-specific requirements for data science teams.

Documented, validated, and corrected outputs from automated analytics and reporting pipelines, ensuring annotation accuracy before final reporting. Contributed to the establishment of annotation and documentation standards across analytical workflows. Collaborated cross-functionally to resolve annotation ambiguities and align output quality for AI and data science initiatives. • Reviewed and corrected process outputs for accuracy and guideline compliance. • Created human-readable narratives describing process workflows step-by-step. • Coordinated team-wide adoption of annotation and quality documentation practices. • Aligned annotation deliverables with project-specific requirements for data science teams.

2020 - 2021

Data Annotation & Market Intelligence Analyst (Intern)

OtherTextClassification

Reviewed and labeled public sector datasets, identifying patterns, anomalies, and trends across multi-dimensional records. Produced structured, quality-assured outputs consistent with annotation best practices for data analytics. Ensured that labeling maintained standardized language and reproducible formatting for downstream machine learning applications. • Labeled trends, anomalies, and categorical outcomes in large tabular datasets. • Maintained annotation consistency and accuracy for analytical records. • Produced structured reports adhering to annotation formatting rules. • Supported pattern recognition and labeling for public sector data projects.

Reviewed and labeled public sector datasets, identifying patterns, anomalies, and trends across multi-dimensional records. Produced structured, quality-assured outputs consistent with annotation best practices for data analytics. Ensured that labeling maintained standardized language and reproducible formatting for downstream machine learning applications. • Labeled trends, anomalies, and categorical outcomes in large tabular datasets. • Maintained annotation consistency and accuracy for analytical records. • Produced structured reports adhering to annotation formatting rules. • Supported pattern recognition and labeling for public sector data projects.

2019 - 2020

Global Suicide Rate Analysis — Structured Labelling & EDA

OtherTextClassification

Cleaned, standardized, and labeled multi-country statistical datasets by outcome, demographics, and causal features. Built annotated datasets to support regression modeling and trend analysis in global suicide research. Communicated annotated results through structured Power BI dashboards for non-technical audiences. • Labeled outcome categories and causal indicators in large tabular datasets. • Created consistent, analysis-ready data for machine learning modeling. • Enabled accessible communication of classification findings via dashboards. • Supported research objectives through clear, accurate data annotation.

Cleaned, standardized, and labeled multi-country statistical datasets by outcome, demographics, and causal features. Built annotated datasets to support regression modeling and trend analysis in global suicide research. Communicated annotated results through structured Power BI dashboards for non-technical audiences. • Labeled outcome categories and causal indicators in large tabular datasets. • Created consistent, analysis-ready data for machine learning modeling. • Enabled accessible communication of classification findings via dashboards. • Supported research objectives through clear, accurate data annotation.

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Education

Work History