Faith Auta

Results-driven Data Annotation and AI Evaluation Specialist with proven experience in assessing, labeling, and validating datasets for machine learning applications. Adept at evaluating AI-generated outputs using structured rubrics, ensuring high levels of accuracy, consistency, and contextual relevance. Brings a strong academic and professional foundation in microbiology and healthcare research, enabling precise evaluation of complex, domain-specific content.

Recognized for exceptional attention to detail, critical thinking, and the ability to provide clear, actionable feedback to improve AI model performance.

AI Data Annotation Specialist - Data Lens

(2026-01)

• Evaluate and annotate large-scale text datasets to improve AI model performance across diverse subject areas
• Apply detailed annotation rubrics to assess relevance, coherence, and factual accuracy of AI-generated outputs
• Identify inconsistencies, edge cases, and ambiguous data, ensuring high-quality and reliable training datasets
• Provide structured, actionable feedback to enhance model reasoning and response quality
• Maintain high accuracy and consistency while working independently in a remote environment
• Contribute to continuous improvement of annotation processes through pattern recognition and quality insights

Lecturer II / Assistant Lecturer (Microbiology) - Joseph Sarwuan Tarka University

(2022-01)

• Evaluate and score student work using structured grading rubrics, ensuring fairness, consistency, and accuracy
• Analyze and interpret complex scientific and healthcare-related content for academic and research purposes
• Manage, validate, and maintain large datasets including academic records and research data
• Communicate complex information clearly in structured, written formats
• Apply critical thinking to assess quality, accuracy, and completeness of submitted work

Graduate Research Assistant (Microbiology) - Joseph Sarwuan Tarka University

(2017-01 - 2022-12)

• Collected, cleaned, and validated research datasets within healthcare and microbiological studies
• Critically evaluated scientific data and literature for accuracy, consistency, and relevance
• Identified anomalies and inconsistencies in datasets through detailed analytical review
• Produced structured reports translating raw data into clear, evidence-based insights
• Ensured integrity and reliability of data used in research conclusions

M.Sc. - Microbiology - University of Ilorin

B.Sc. - Microbiology - University of Maiduguri