Muhammad Azeen

My independent case study, Tracking Data Quality and Anomaly Detection Rule Design, examines how browser tracking restrictions can lead to undercounted conversions. I outlined a server-side tagging approach to address the data gap.

I also analyzed how combining subscription and one-time purchases in one conversion action can hide different behaviors. I proposed separate signals for each.

For anomaly detection, I designed decision logic that flags a placement only when click-through rate rises unusually while conversion rate falls. I considered false alerts from genuine traffic surges and proposed an allowlist for valid sources.

At Dark Defensive, I proposed assessments across analytics, conversion measurement, search visibility, and HubSpot lead journeys, and researched the areas within the access provided. I’m building my technical base in SQL, Python, and Git, practicing Linux after completing a full course, and applying C++ programming fundamentals from coursework and labs.