Abdallah Zubedi — Data Analyst, Toronto, Canada

This study follows a single subject across ten years, three continents, and two disciplines. The subject began in marketing, developed an unusual attachment to measurement, and is currently converting that attachment into a career in data science. Observed effects include a +600% lift in email engagement, a +40% lift in qualified lead volume, and a persistent habit of photographing wildlife. The instruments turn out to be indifferent to the industry: the models that scored leads would read sensor logs and forecasts the same way. The pattern holds. Replication is invited.

Experience

  • Kuala Lumpur, Malaysia (2016 to 2020) Treatment period one. BSc Marketing at Lancaster / Sunway (2:1), a peer-reviewed article, "Assessing Consumer Skepticism Towards Sustainable Marketing" (Journal of Marketing Management and Consumer Behavior, 2020), its fieldwork set in the energy sector, and a first data role at Tapway: 140+ qualified leads pulled from overlooked segments.
  • Nairobi, Kenya (2020 to 2021) Treatment period two. Marketing and business development at Eccentric: +18% regional FMCG sales through gamified activations, and a data-driven omnichannel funnel that lifted conversions ~30%.
  • Toronto, Canada (2021 to present) Treatment period three. Master of Management (MMIE) at Queen's, GPA 4.11/4.3, 2nd place in the Loblaw Innovation Challenge. Then Communitech, Moonstone, and AEC Daily, where the measurable effects got large.

Findings

  • +600% click-through rate Email engagement responds violently to systematic testing. Redesigned and systematically optimized the HTML email templates at AEC Daily, testing subject lines, layout, and send timing. Click-throughs went from about 100 to 700 per send.
  • +40% qualified leads Lead quality is an engineering problem, not a sales complaint. Automated the reporting workflows in Excel and Power Automate and tightened data validation (about 30% faster) at AEC Daily. Faster, cleaner data surfaced better prospects and lifted qualified-lead volume roughly 40%.
  • 81% test accuracy Churn is predictable if you engineer the right features. Google Advanced Data Analytics capstone: a full ML pipeline on the 14,999-record Waze churn dataset. EDA, hypothesis testing, feature engineering, then a model bake-off with XGBoost as champion.
  • 2nd place, national Strategy survives contact with judges when it is built on data. A food-waste-reduction strategy for the Loblaw Innovation Challenge: market research, financial modelling, and consumer data behind a multi-scenario plan. Placed 2nd nationally, among Canadian Master's cohorts.

Skills

SQL (daily use), Python (pandas, scikit-learn), R (ggplot2, tidyverse), Visualization (Power BI, Tableau, Looker), Attribution (regression-backed), A/B testing (design and analysis), Automation (Power Automate, Claude API), Marketing ops (GA4, CRM, email)

Education

  • BSc (Hons) Marketing, Lancaster University, Malaysia campus (2016–2020)
  • Master of Management (MMIE), Queen's University, Kingston, Canada (2021–2022)
  • MSc in Analytics (Computational Data / Data Science), Georgia Institute of Technology, Atlanta, USA (2026–present)

Publication

Zubedi, A. T. A., Nezakati, H., & Abdi, L. (2020). "Assessing Consumer Skepticism Towards Sustainable Marketing." Journal of Marketing Management and Consumer Behavior. Archived copy.

Contact

dalla_zubedi@outlook.com · linkedin.com/in/abdallah-zubedi