Brydon Parker

Toronto, ON

• —Design and run the experimentation, causal inference, and measurement programs used to evaluate product, growth, pricing, and AI investments across the business — 100+ experiments annually.
• —Built the forecasting, opportunity-sizing, attribution, and incrementality frameworks that prioritized growth initiatives and informed the investment decisions behind scaling from $1M to $40M ARR.
• —Built the recommendation systems and evaluation frameworks powering Stanley for Instagram, an AI-powered creator coaching platform ($1.5M+ ARR) — shipping 3 statistically significant production improvements and surfacing 3 more high-confidence opportunities, contributing to 8/10 user-satisfaction scores for AI ideation and recommendations.
• —Scaled the function from its first hire into a team of 8 across data science and data engineering, setting technical direction while staying hands-on with the highest-leverage modeling and measurement work.

Toronto, ON

• —First data hire at Stan; built the company's analytics, experimentation, and AI capabilities from the ground up while helping scale the business from $1M to $40M ARR.
• —Built Stan's data & experimentation platform from scratch — data infrastructure, executive reporting, experimentation systems, forecasting, and company-wide measurement frameworks — enabling hundreds of experiments annually.
• —Defined the company's core metrics and launched Stanley, Stan's first AI-powered creator coach — establishing the foundation for product decision-making and the company's broader AI strategy.

Toronto, ON

• —Lead Data Scientist for the launch of Shopify's $5 Starter Plan — driving financial modeling, opportunity sizing, experimentation, and success measurement for one of Shopify's most significant growth initiatives.
• —Built self-serve analytics and decision-support systems used across Shopify's Growth org to inform product, investment, and go-to-market decisions.
• —Mentored data scientists and product teams on experimentation, causal inference, opportunity sizing, and machine learning — raising analytical rigor across the org.

Toronto, ON

• —Data science lead on a retail demand-forecasting system (PyTorch on Azure Databricks) for 100k+ items and $1B+ in annual sales, cutting inventory costs ~30% vs. baseline.
• —Drove model enhancements that reduced forecast error by $10M+ and improved performance 5% over baseline, while supervising a team of 5 data scientists.
• —Built deep-learning + NLP risk-detection models (TensorFlow / PyTorch) achieving PR-AUC > 0.8 across most risk classes for major retail and financial-services clients.