My background in data analytics has equipped me with core competencies directly transferable to AI training and data lab
My background in data analytics has equipped me with core competencies directly transferable to AI training and data labeling work. Through projects involving real-world datasets; including Google Analytics 360 data, Nigerian macroeconomic records, and telecom customer behaviour data, I have developed a disciplined approach to data quality assessment, anomaly detection, and structured feedback documentation. These skills mirror the precision required in prompt evaluation, response quality rating, and RLHF feedback tasks central to AI model improvement. My experience building and validating machine learning pipelines; including a logistic regression churn model at 86% accuracy and a controlled A/B testing framework has sharpened my ability to evaluate model outputs critically and identify where predictions or responses fall short of expected standards. Combined with a postgraduate research background in user behaviour analysis and personalisation, I bring both technical grounding and contextual judgment to AI training tasks requiring nuanced evaluation of language, intent, and output quality.