As a Data Analyst at TELUS International, I was responsible for evaluating and annotating real-world user queries to support Apple’s search engine. My main objective was to ensure that the datasets used in machine learning pipelines were clean, consistent, and relevant. This involved reviewing large volumes of data, applying strict quality standards, and identifying anomalies or ambiguous cases requiring deeper analysis.

I regularly documented issues through structured ticketing systems, providing clear feedback that helped refine internal annotation tools and processes. I worked autonomously, using internal resources and evolving guidelines to adapt to changing task requirements. Over time, I developed a strong sense for detail and data reliability, maintaining a consistent track record of high accuracy and throughput.

The role sharpened my understanding of the link between raw data and model outcomes — particularly in real-world systems that impact millions of users. It also gave me hands-on exposure to data governance principles and the practical importance of reproducibility, traceability, and auditability in AI systems. These insights continue to shape my approach to responsible data science and system evaluation.