Dodle Vinay

Key Skills

Software

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Text

Document

Image

Top Label Types

Classification

Question Answering

Freelancer Overview

I am passionate about contributing to the development of AI through high-quality data labeling and annotation. With a strong academic background in Artificial Intelligence and Computer Science, I have hands-on experience working with data management, manipulation, and analytics, particularly through my Employment Management System project where I managed and processed large sets of employee data using Python, MySQL/SQLite, pandas, and GUI/web frameworks like tkinter and flask. My technical skills in programming, database management, and data processing, combined with certifications in Java and SQL, enable me to efficiently handle and annotate diverse datasets for AI training. I am eager to apply my attention to detail, analytical mindset, and commitment to data quality to help build robust AI systems.

Labeling Experience

Freelance/Coursework Data Tasks – Spreadsheet Annotation and Cleaning

Scale AITextClassification

I completed structured spreadsheet-based tasks focused on sorting, categorizing, and cleaning text fields to prepare datasets for analysis and visualization. These activities required strict adherence to instructions and detail-oriented execution, often as part of freelance work and coursework. My support extended to explaining annotation concepts and quality checks to peers. • Cleaned and standardized text data in spreadsheets for analysis • Sorted and categorized data according to assignment specifications • Provided peer support on labeling guidelines and dataset preparation • Worked via online job and gig platforms to deliver annotation tasks

I completed structured spreadsheet-based tasks focused on sorting, categorizing, and cleaning text fields to prepare datasets for analysis and visualization. These activities required strict adherence to instructions and detail-oriented execution, often as part of freelance work and coursework. My support extended to explaining annotation concepts and quality checks to peers. • Cleaned and standardized text data in spreadsheets for analysis • Sorted and categorized data according to assignment specifications • Provided peer support on labeling guidelines and dataset preparation • Worked via online job and gig platforms to deliver annotation tasks

2025

AI & ML Student Projects – Image Data Preparation & Labeling

Scale AIImageClassification

I participated in computer-vision practice tasks where I assisted with image data preparation and quality checks for ML pipelines. My tasks included loading, inspecting, and transforming raw images to ensure they were suitable for annotation and machine learning. This process involved following guidelines for image handling and documenting edge cases for consistent dataset reuse. • Loaded and validated image files for annotation readiness • Checked image formats, sizes, and integrity for ML compatibility • Documented best practices and issues encountered during annotation prep • Supported team in creating small labeled image sets for experiments

I participated in computer-vision practice tasks where I assisted with image data preparation and quality checks for ML pipelines. My tasks included loading, inspecting, and transforming raw images to ensure they were suitable for annotation and machine learning. This process involved following guidelines for image handling and documenting edge cases for consistent dataset reuse. • Loaded and validated image files for annotation readiness • Checked image formats, sizes, and integrity for ML compatibility • Documented best practices and issues encountered during annotation prep • Supported team in creating small labeled image sets for experiments

2025

AI & ML Student Projects – Document Annotator (Law RAG Chatbot)

Scale AIDocumentQuestion Answering

In a law RAG chatbot project, I organized legal document datasets and defined relevant sections for annotation and retrieval evaluation. Passages were marked as ground-truth answers, enabling accurate performance assessment of retrieval and generation. These efforts supported the creation of high-quality, reference-checked evaluation data. • Segmented legal documents into sections for labeling • Labeled ground-truth passages for question answering tasks • Ensured consistent annotation and documentation for reproducibility • Assisted in evaluating chatbot responses using labeled data

In a law RAG chatbot project, I organized legal document datasets and defined relevant sections for annotation and retrieval evaluation. Passages were marked as ground-truth answers, enabling accurate performance assessment of retrieval and generation. These efforts supported the creation of high-quality, reference-checked evaluation data. • Segmented legal documents into sections for labeling • Labeled ground-truth passages for question answering tasks • Ensured consistent annotation and documentation for reproducibility • Assisted in evaluating chatbot responses using labeled data

2025

AI & ML Student Projects – Data Labeler & Evaluator

Scale AITextClassification

I manually labeled and cleaned text data to fine-tune a Transformer-based sentiment analysis model using movie reviews. My responsibilities included ensuring consistent label definitions and resolving ambiguous cases while preparing evaluation datasets. I regularly compared model predictions with gold labels to measure performance metrics and documented annotation rules for reproducibility. • Labeled sentiment (positive, negative, neutral) for textual movie review data • Designed small evaluation sets to check model accuracy, precision, and recall • Maintained annotation guidelines and resolved edge cases for clear instructions • Analyzed systematic errors to refine both labels and model outputs

I manually labeled and cleaned text data to fine-tune a Transformer-based sentiment analysis model using movie reviews. My responsibilities included ensuring consistent label definitions and resolving ambiguous cases while preparing evaluation datasets. I regularly compared model predictions with gold labels to measure performance metrics and documented annotation rules for reproducibility. • Labeled sentiment (positive, negative, neutral) for textual movie review data • Designed small evaluation sets to check model accuracy, precision, and recall • Maintained annotation guidelines and resolved edge cases for clear instructions • Analyzed systematic errors to refine both labels and model outputs

2025

Education

Work History