Shraddha Debata

Key Skills

Software

Mercor

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Document

Image

Text

Video

Top Label Types

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Point Key Point

Segmentation

Classification

Prompt Response Writing SFT

Freelancer Overview

I am a data professional with over 3 years of hands-on experience in data annotation, labeling, and validation for AI and machine learning projects. My background spans the full data lifecycle, from creating and refining labeling guidelines to executing multi-modal data annotation across images, videos, audio, and text. I have designed and operationalized quality review workflows and dashboards to ensure accuracy, consistency, and edge-case coverage in large-scale datasets, directly improving model confidence and performance. My technical toolkit includes Python, SQL, Tableau, and cloud platforms like GCP and AWS, enabling me to automate data pipelines and validation processes for faster, more reliable AI training data. I thrive in fast-paced, cross-functional environments, collaborating closely with ML, analytics, and product teams to translate complex requirements into actionable annotation strategies and deliver high-quality, model-ready datasets for domains such as computer vision, social media, manufacturing, and sensor data. My strong attention to detail, communication skills, and quality-first mindset drive my ability to consistently deliver impactful results on tight timelines.

Labeling Experience

Data Annotation Analyst

MercorImageBounding BoxPoint Key Point

Led data annotation and review workflow supporting AI/ML training initiatives, translating complex requirements into clear labelling rules, decision trees and validation criteria/rubrics. Ensured accuracy, consistency, and edge-case coverage across large datasets by implementing structured review processes, peer-check framework, and automated quality dashboards, resulting in faster turnaround times and improved model confidence.

Led data annotation and review workflow supporting AI/ML training initiatives, translating complex requirements into clear labelling rules, decision trees and validation criteria/rubrics. Ensured accuracy, consistency, and edge-case coverage across large datasets by implementing structured review processes, peer-check framework, and automated quality dashboards, resulting in faster turnaround times and improved model confidence.

2025

Education

Work History