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ClassificationExperienced in the end to end lifecycle of AI training data, I specialize in fine tuning Large Language Models (LLMs) through high-quality data annotation and rigorous evaluation. My work involves applying complex rubrics to assess model outputs for factuality, safety, and reasoning, often utilizing tools like Hubstaff for precision time-tracking and platforms like Outlier for project execution. I set myself apart by blending my background as a Data Strategist with the technical nuances of Reinforcement Learning from Human Feedback (RLHF), ensuring that training datasets are not just labeled, but architected to improve model performance. By focusing on the structural integrity of data and the logical consistency of AI responses, I bridge the gap between raw data collection and the deployment of sophisticated, production-ready AI systems.
data labeling task to help train AI models to realistically erase objects from video footage.
As a Data Analyst Trainer at TrevoTech Academy, I instructed and mentored aspiring Data Analysts in analyzing and interpreting datasets. I provided guidance on industry best practices and analytical techniques for extracting relevant insights from structured and unstructured data. My work contributed to preparing analysts to accurately classify, interpret, and report data for business purposes. • Delivered classroom and hands-on instruction on industry-relevant data analysis processes. • Monitored progress and provided targeted feedback to ensure comprehension of data annotation and labeling quality. • Emphasized the importance of proper labeling, classification, and categorization in data-driven environments. • Facilitated interactive exercises involving classification of text and business data using best practice tools.
Bachelor of Technology, Chemistry
Data Analyst
Data Analyst (Contractor)
