Emil Ortiz

• Analyze and validate quality of 1,400+ multilingual data records weekly across telecommunications, legal, and media sectors using systematic evaluation frameworks • Achieve 94.8% error detection accuracy through application of DQF (Dynamic Quality Framework) and MQM (Multidimensional Quality Metrics) statistical methodologies • Conduct comparative analysis and ranking of AI-generated outputs, evaluating relevance, accuracy, and business value across diverse content domains • Develop and maintain quality assessment frameworks for specialized sectors including legal terminology, technical documentation, and marketing analytics • Manage data consistency databases ensuring standardization and efficiency across recurring analysis projects and client deliverables • Coordinate with international analysts across 6 countries to establish unified data quality standards and resolve complex analytical challenges • Generate detailed analytical reports documenting error patterns, data trends, and actionable improvement recommendations for model optimization • Lead quarterly calibration sessions achieving 91%+ inter-rater agreement across data quality team of 13 analysts Key Achievement: Implemented quality improvement processes reducing data errors by 42% and increasing client satisfaction scores by 36%; developed comprehensive data quality framework for legal content reducing errors by 58%

• Processed and annotated 62,000+ multilingual data samples for machine learning applications including NER, sentiment analysis, and classification systems • Maintained 87.5% data consistency through disciplined guideline adherence and continuous quality monitoring across high- volume projects • Classified data across multiple dimensions including topic, sentiment, intent, formality level, and target audience using structured taxonomies • Evaluated AI model outputs identifying errors in syntax, semantics, and cultural adaptation using statistical error analysis • Conducted systematic fact-checking verifying claims against authoritative multilingual data sources across telecommunications and digital media • Performed A/B testing analysis to optimize data quality and task completion rates across various content domains • Collaborated with data scientists providing analytical insights on model performance and training data optimization strategies Key Achievement: Created standardized data template library containing 600+ validated schemas improving data processing velocity by 39%

• Reviewed and validated 47,000+ data records for conversational AI, text classification, and information extraction analytics projects • Achieved 84.6% quality approval rate on first-pass reviews through meticulous attention to data accuracy and completeness • Assessed multilingual datasets for accuracy, semantic consistency, and structural appropriateness across business applications • Performed data validation ensuring format compliance, completeness, and adherence to project specifications and quality standards • Applied structured evaluation rubrics to classify data quality and identify areas requiring remediation • Generated quality reports documenting error frequencies, pattern analysis, and performance trends for stakeholder review • Supported training initiatives mentoring new team members on data validation tools and quality best practices

AI Content Evaluation Professional

European AI Training Institute

Advanced Data Quality Assessment

Institute of Translation and Interpreting

Localization Quality Assurance

Localization Institute Europe

Data Science for NLP Applications

Coursera (Imperial College London)