Paul Yonghao Li
Lead Data & Machine Learning Engineer @Canadian Tire Corporation
Signup · Get unlimited contacts
WORK HISTORY
Lead Data & Machine Learning Engineer @Canadian Tire Corporation
Toronto, ON, CA
Own and operate production ML pipelines end-to-end, from feature generation and training to batch inference, monitoring, and automated retraining- Design scalable Spark-based workflows on Azure Databricks, handling late-arriving data, schema evolution, and large historical backfills- Implement ML reliability practices including data quality validation, drift detection, performance monitoring, and alerting- Deploy and manage models using MLflow, with controlled promotion and reproducibility across environments- Partner with data scientists and stakeholders to productionize models while enforcing engineering and governance standards.
EDUCATION
Schulich School of Business - York University
Master of Business Administration - MBA, Dual specialization in Marketing and Entrepreneurial Studies
Georgia Institute of Technology
Master of Science - MS, Computer Science
ABOUT PAUL YONGHAO LI
I’m a Lead Machine Learning Engineer with a background spanning analytics, data science, and large-scale ML systems. I design and operate production-grade ML pipelines that turn messy enterprise data into reliable, decision-ready outputs.My work sits at the intersection of modeling, data engineering, and MLOps—from feature engineering and training workflows to batch inference, monitoring, and automated retraining. I’ve spent the last several years building Spark- and Databricks-based ML systems that support forecasting, segmentation, and predictive use cases at scale.I care deeply about reliability and maintainability: schema enforcement, data quality checks, lineage, drift detection, and controlled model promotion are not “nice to haves,” but core system requirements.Over time, I’ve progressed from analytics and modeling into technical leadership and ML engineering, owning end-to-end systems and enabling other data scientists to ship models safely and repeatedly.Currently focused on building scalable ML platforms and production systems, and interested in roles that emphasize ML infrastructure, applied modeling at scale, and real-world constraints.
This profile is compiled from publicly available professional sources. Unifers is not affiliated with or endorsed by LinkedIn. Request removal of this profile.