Evans Rotich

Key Skills

Software

AWS SageMaker

Labelbox

Internal/Proprietary Tooling

Label Studio

Top Subject Matter

No subject matter listed

Top Data Types

Video

Computer Code Programming

Top Label Types

Emotion Recognition

Action Recognition

Tracking

Text Summarization

Computer Programming Coding

Freelancer Overview

I am a detail-oriented Data Labeling Specialist with over three years of hands-on experience annotating images, video, and text to support AI and machine learning projects. My expertise spans computer vision tasks such as object detection and segmentation, as well as natural language processing tasks including text classification, sentiment analysis, and named entity recognition. I am highly proficient with industry-standard annotation tools like Labelbox, CVAT, SuperAnnotate, and Amazon SageMaker Ground Truth, and I have a strong track record of developing clear labeling guidelines and taxonomies to ensure consistent, high-quality data. I am also skilled in basic Python scripting for data preparation and quality assurance workflows. My collaborative approach and fluency in English enable me to deliver accurate, reliable training data for diverse projects, and I am committed to maintaining rigorous quality standards throughout the annotation process.

Labeling Experience

Programming Data Annotation Specialist

LabelboxComputer Code ProgrammingComputer Programming Coding

I worked as a Programming Data Annotation Specialist on a large-scale AI training project using Labelbox, where I labeled and reviewed thousands of code samples in Python, Java, and JavaScript. The project focused on improving AI models for code generation, bug detection, and function understanding. My tasks included span annotation (highlighting variables, functions, and logic structures), classification of code types, pairwise comparison of code completions, and syntax-level token tagging. I also identified bugs and evaluated AI-generated explanations for technical accuracy. The project involved tens of thousands of samples, and quality was ensured through reviewer audits, inter-annotator agreement checks, and strict adherence to evolving annotation guidelines.

I worked as a Programming Data Annotation Specialist on a large-scale AI training project using Labelbox, where I labeled and reviewed thousands of code samples in Python, Java, and JavaScript. The project focused on improving AI models for code generation, bug detection, and function understanding. My tasks included span annotation (highlighting variables, functions, and logic structures), classification of code types, pairwise comparison of code completions, and syntax-level token tagging. I also identified bugs and evaluated AI-generated explanations for technical accuracy. The project involved tens of thousands of samples, and quality was ensured through reviewer audits, inter-annotator agreement checks, and strict adherence to evolving annotation guidelines.

2024

Freelance Data Annotation Specialist

Aws SagemakerVideoEmotion RecognitionAction Recognition

Provided data annotation services for AI projects, labeled 20,000+ images and 5,000+ video frames.- Conducted text annotation (sentiment, NER) on U.S. English content.- Managed workflows in Labelbox an

Provided data annotation services for AI projects, labeled 20,000+ images and 5,000+ video frames.- Conducted text annotation (sentiment, NER) on U.S. English content.- Managed workflows in Labelbox an

2021

Image Data Annotation & Content Review Specialist

Internal Proprietary ToolingImageBounding BoxClassification

Contributed to image-based data annotation and quality review projects supporting AI model development. Responsibilities included reviewing labeled image content for accuracy, applying appropriate classification tags, and verifying that responses aligned with visible information in the image. Extracted relevant text and visual details from screenshots, scanned documents, and digital imagery to validate dataset entries. Identified inconsistencies, corrected labeling errors, and ensured adherence to structured annotation guidelines while maintaining consistent productivity and quality standards.

Contributed to image-based data annotation and quality review projects supporting AI model development. Responsibilities included reviewing labeled image content for accuracy, applying appropriate classification tags, and verifying that responses aligned with visible information in the image. Extracted relevant text and visual details from screenshots, scanned documents, and digital imagery to validate dataset entries. Identified inconsistencies, corrected labeling errors, and ensured adherence to structured annotation guidelines while maintaining consistent productivity and quality standards.

2024 - 2024

Recorded Customer Support Call Annotation for Speech AI Quality Improvement

Label StudioAudioAudio Recording

Worked on a completed audio labeling project involving recorded US English customer support calls from a subscription-based service company. The goal was to prepare clean training data for speech recognition and call analysis models. I used Label Studio to label speaker turns and timestamp each utterance, Audacity to review unclear sections, inspect waveforms, and replay difficult audio, and Google Sheets to track labeling batches, QA notes, and common error patterns. My tasks included verbatim transcription of short call segments, separating agent and customer speech, marking interruptions and overlapping speech, tagging background noise, and labeling call outcomes such as resolved issue, escalation, cancellation request, and billing inquiry. I also reviewed completed samples for consistency, corrected misheard words, and flagged low-audio-quality files that could not be labeled reliably.

Worked on a completed audio labeling project involving recorded US English customer support calls from a subscription-based service company. The goal was to prepare clean training data for speech recognition and call analysis models. I used Label Studio to label speaker turns and timestamp each utterance, Audacity to review unclear sections, inspect waveforms, and replay difficult audio, and Google Sheets to track labeling batches, QA notes, and common error patterns. My tasks included verbatim transcription of short call segments, separating agent and customer speech, marking interruptions and overlapping speech, tagging background noise, and labeling call outcomes such as resolved issue, escalation, cancellation request, and billing inquiry. I also reviewed completed samples for consistency, corrected misheard words, and flagged low-audio-quality files that could not be labeled reliably.

2022 - 2022

Education

Work History