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Freelancer Overview

During my co-op at a chemical manufacturing company, I contributed to an NLP project that automated the analysis of safety incident reports. I labeled and categorized over 5,000 incident descriptions, identifying hazard types, root causes, and corrective actions. My chemical engineering background proved invaluable, I caught labeling inconsistencies where annotators misclassified technical incidents (like "thermal runaway" vs. "overheating"), improving data quality by 30%. The impact: our labeled dataset trained a model that reduced safety report processing time from weeks to hours, enabling faster identification of recurring hazards across facilities. This directly contributed to implementing preventive measures 3x faster than the previous manual review process. My unique combination of domain expertise, attention to detail, and understanding of how data quality affects ML outcomes allowed me to bridge the gap between safety engineers and data scientists, ensuring the training data reflected real-world chemical process risks accurately.

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