Data Labeling / AI Training Experience
Over the past two years, I've worked on several data labeling and annotation projects spanning finance, business, and general AI training pipelines. 1. Financial Document Text Annotation (NLP) Annotated large volumes of financial documents — including earnings reports, invoices, and loan agreements — tagging key entities such as dates, monetary values, parties, and clauses. This was used to train NLP models for automated document parsing and contract review systems. 2. Business Image & Chart Labeling Labeled and classified business-related images including financial charts, infographics, scanned receipts, and ID documents. Tasks involved bounding box annotation, OCR validation, and category classification to support computer vision models used in fintech applications. 3. Data Quality Review & Auditing Served as a quality reviewer on annotation teams, auditing labeled datasets for consistency, accuracy, and guideline compliance. Flagged edge cases, resolved labeling conflicts, and provided feedback that improved inter-annotator agreement scores across the project. 4. Sentiment & Intent Annotation (NLP) Annotated customer feedback, financial news snippets, and support chat logs for sentiment, intent, and topic classification — contributing to training datasets for business intelligence and customer experience AI tools.