Ritesh Jha

Key Skills

Software

CVAT

Top Subject Matter

No subject matter listed

Top Data Types

Image

Top Label Types

Bounding Box

Polygon

Segmentation

Freelancer Overview

I am a Data Science student with hands-on experience in building and deploying machine learning models, including a house price prediction system and a backend question answering service powered by large language models. My background includes extensive work with Python, Pandas, NumPy and Scikit-learn for data preprocessing, feature engineering and model evaluation, as well as SQL for structured data management. I have developed end-to-end workflows for data cleaning, annotation, and transformation, and I am comfortable designing scalable backend systems for AI inference and intelligent applications. My strengths lie in analytical thinking, attention to detail, and a passion for creating robust AI solutions that rely on high-quality, well-labeled training data. I am eager to contribute to projects involving data labeling, annotation, and the development of reliable AI datasets, especially in domains like healthcare, NLP, and real-world data analysis.

Labeling Experience

Autonomous Driving Image Annotation Project

CVATImageBounding BoxPolygon

I contributed to a large-scale computer vision project focused on improving object detection models for autonomous driving systems. The goal was to accurately label real-world road scene images to enhance model performance in complex traffic environments. My Role & Responsibilities: Annotated high-resolution street images using bounding boxes, polygons, and semantic segmentation Labeled vehicles, pedestrians, cyclists, traffic signs, lane markings, and road boundaries Ensured strict adherence to project-specific annotation guidelines Performed quality checks to maintain high annotation accuracy and consistency Collaborated with QA teams to correct edge cases and ambiguous scenarios

I contributed to a large-scale computer vision project focused on improving object detection models for autonomous driving systems. The goal was to accurately label real-world road scene images to enhance model performance in complex traffic environments. My Role & Responsibilities: Annotated high-resolution street images using bounding boxes, polygons, and semantic segmentation Labeled vehicles, pedestrians, cyclists, traffic signs, lane markings, and road boundaries Ensured strict adherence to project-specific annotation guidelines Performed quality checks to maintain high annotation accuracy and consistency Collaborated with QA teams to correct edge cases and ambiguous scenarios

2025 - 2025

Education

Work History