DEBORAH ANOSA

I’m a precise, methodical, and intellectually curious professional with a Second Class Upper degree in Electrical Electronics Engineering, drawn to AI annotation because it combines rigorous attention to detail with making complex systems work better. My engineering training taught me to treat accuracy as non-negotiable—because a single misclassification can cascade downstream. I bring structured thinking to labelling, reviewing, and interpreting data, with a strong focus on standards, guidelines, and edge cases.

In my Technical Data & Annotation Support role at Nestlé Nigeria PLC (NYSC Posting), I systematically collected, reviewed, and categorised equipment performance data while maintaining data integrity across high-volume documentation. I quality-checked operational records for accuracy, completeness, and internal consistency, and applied structured evaluation criteria to annotate findings with standardised classifications. I also identified recurring fault signatures and anomalies, contributing to a 20% reduction in unplanned machine downtime, and supported energy efficiency improvements by helping achieve a 15% reduction in energy costs.

At African Development Institute of Research Methodology (ADIRM), I designed and deployed a structured data classification and labelling system for the research database—reducing retrieval errors and improving searchability by 30%. I audited and re-annotated existing records, developed assessment tools, and drafted annotation guidelines to reduce inter-annotator inconsistency. I’m now seeking a postgraduate AI annotation internship where I can contribute to machine learning data pipelines, deepen my understanding of AI training workflows, and bring the same care and consistency to labelling tasks I’ve delivered across every data-facing role.