naresh chandra

I have over 9 years of experience in data analytics and AI-related projects, with a strong foundation in data curation, labeling, and quality assurance for machine learning systems. My career has centered around building, validating, and maintaining high-quality datasets that improve the accuracy and fairness of AI models.

In my recent roles, I’ve worked on data annotation projects supporting NLP and computer vision models, where I was responsible for:

• Annotating and labeling large volumes of text and image data with precision and consistency.

• Implementing quality control measures, cross-verifying annotations, and maintaining accuracy above 98%.

• Collaborating with global teams across time zones to ensure alignment with project goals and annotation guidelines.

• Leveraging tools such as [insert tools if you know them, e.g., Labelbox, CVAT, or custom annotation platforms] to manage workflows and optimize labeling efficiency.

Beyond annotation, I’m deeply interested in how data quality influences AI model performance. I’ve participated in initiatives that analyzed mislabeled data, improved taxonomy structures, and streamlined annotation pipelines — helping reduce rework and improve throughput.

I’m particularly passionate about contributing to the future of responsible AI, ensuring datasets are inclusive, unbiased, and representative. My blend of analytical mindset, attention to detail, and commitment to data integrity makes me well-suited for a role where quality data powers intelligent systems.

In addition to my technical skills, I bring strong project management experience, having coordinated deliverables, met tight deadlines, and ensured adherence to quality standards in fast-paced environments.