Jaya Iskandar

Key Skills

Software

Roboflow

Don't disclose

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Text

Top Label Types

Bounding Box

Text Summarization

Translation Localization

Freelancer Overview

I have experience in data labeling and AI training data preparation, ensuring accuracy and consistency across text, image, and structured datasets. I am skilled at following detailed guidelines, resolving ambiguities, and maintaining high-quality outputs that support reliable machine learning models. With a background in software and product engineering, I bring technical awareness of how training data impacts downstream AI systems. This combination of precision, adaptability, and technical insight sets me apart in contributing to data annotation projects at scale.

Labeling Experience

Voice Classification

Don T DiscloseAudioAudio Recording

Scope: Contributed to an AI training project focused on improving speech recognition and audio quality models. The task involved listening to voice recordings and evaluating them against a defined rubric. Specific Task: Classified audio samples by quality, clarity, and accuracy of speech based on detailed annotation guidelines. Applied labels to indicate issues such as background noise, mispronunciation, or unclear articulation. Quality Measures: Maintained strict adherence to labeling rules, cross-checked ambiguous cases, and consistently met accuracy and throughput benchmarks set by the project. Ensured high-quality annotations to support model training and validation.

Scope: Contributed to an AI training project focused on improving speech recognition and audio quality models. The task involved listening to voice recordings and evaluating them against a defined rubric. Specific Task: Classified audio samples by quality, clarity, and accuracy of speech based on detailed annotation guidelines. Applied labels to indicate issues such as background noise, mispronunciation, or unclear articulation. Quality Measures: Maintained strict adherence to labeling rules, cross-checked ambiguous cases, and consistently met accuracy and throughput benchmarks set by the project. Ensured high-quality annotations to support model training and validation.

2025 - 2025

Image Classification

RoboflowImageBounding Box

Scope: Supported a computer vision project aimed at distinguishing coffee bean varieties (Arabica, Robusta, Liberica) for model training. Used Roboflow as the primary platform for dataset preparation. Specific Task: Annotated images by drawing bounding boxes around individual coffee beans and labeling them by type. Ensured consistency in annotation across large datasets to enable accurate classification and detection. Quality Measures: Followed dataset annotation standards, reviewed annotations for precision, and validated bounding box placement to minimize labeling errors. Maintained consistency and accuracy to ensure high-quality training data for robust computer vision models.

Scope: Supported a computer vision project aimed at distinguishing coffee bean varieties (Arabica, Robusta, Liberica) for model training. Used Roboflow as the primary platform for dataset preparation. Specific Task: Annotated images by drawing bounding boxes around individual coffee beans and labeling them by type. Ensured consistency in annotation across large datasets to enable accurate classification and detection. Quality Measures: Followed dataset annotation standards, reviewed annotations for precision, and validated bounding box placement to minimize labeling errors. Maintained consistency and accuracy to ensure high-quality training data for robust computer vision models.

2024 - 2024

Education

Work History