Data protection Analyst
In my role as a Data Protection Analyst, I supported project-specific AI data labeling initiatives focused on preparing high-quality datasets for machine learning model training while ensuring strict adherence to data privacy and governance standards. Scope of Work: My responsibilities included labeling, categorizing, and validating both structured and unstructured data such as text, transactional records, and metadata. Tasks involved text classification, sentiment analysis, and entity recognition, where I followed detailed annotation guidelines to ensure consistency and usability for AI model training. I also reviewed datasets to identify and remediate misclassified or sensitive data, ensuring proper handling of personally identifiable information (PII) through masking or exclusion where required. Project Size & Complexity: I worked on large-scale datasets ranging from thousands to millions of data points across multiple systems and environments. The projects often involved iterative labeling cycles, where datasets were continuously refined based on feedback from data scientists and QA teams to improve model performance. I collaborated with cross-functional stakeholders, including AI/ML teams, compliance, and data engineering, to align labeling outputs with business and technical requirements. Quality Measures & Standards: To maintain high-quality outputs, I adhered to strict quality assurance and data governance practices, including: Following standardized annotation guidelines to ensure high accuracy and consistency Performing peer reviews and validation checks to minimize errors and discrepancies Participating in QA processes such as sampling, error tracking, and feedback loops Ensuring compliance with data protection standards by identifying and safeguarding sensitive data (e.g., PII masking and secure handling) Maintaining audit trails and documentation to support traceability and accountability Monitoring labeling quality metrics and continuously improving performance based on feedback Overall, I bring a strong balance of precision in data labeling and a deep understanding of data protection principles, ensuring that datasets are not only accurate and consistent but also secure and compliant for AI model development.