AI Leader

Promoted to Director

• Lead a 15-person full-stack ML team, across hybrid and offshore staffing, delivering enterprise personalization across Sobeys banners.
• Oversee weekly recommendations for 16M customers across email (SFMC), web, and mobile platforms.
• Own the end-to-end pipeline, from Enterprise Data Lake extraction through model development, deployment, and measurement.
• Architect and review scalable ML systems using PySpark and Databricks in Azure while enforcing code quality and production standards.
• Implement MLOps and reliability practices to improve automation and stability.
• Partner with Marketing, Loyalty, and Cloud teams, manage agile delivery through JIRA and PlanView, and provide executive reporting on ROI, costs, and program health.
• Delivered 75% cloud cost reduction, 95% fewer pipeline failures, 4x scale growth, 10x incremental sales lift, and a Bayesian framework for dynamic campaign measurement.

• Build and maintain scalable ML systems for personalized recommendations, in-app advertising, and marketing optimization across hundreds of millions of users.
• Develop production-grade models using Scala and Python with AWS-based infrastructure.
• Design and maintain data engineering pipelines to clean and transform large-scale application log data into ML-ready features.
• Create executive dashboards and visualizations supported by Kubeflow-based ML workflows.
• Operate in a dual role as both Machine Learning Engineer and team lead within the core ML team.
• Manage and mentor a team of 6 MLEs, translating business requirements into technical roadmaps and delivery plans.
• Lead agile planning through JIRA, coordinate cross-functional stakeholders, and produce executive-level progress and impact reports.

• Develop deep learning-based recommendation engines using TensorFlow and PyTorch within Google Cloud Platform.
• Deploy recommendation systems across site personalization, digital advertising, and email customization.
• Enhance search relevance through NLP-based ranking and personalization algorithms.
• Conduct research to evaluate and implement state-of-the-art modeling approaches.
• Collaborate cross-functionally to automate workflows and improve operational processes.
• Work closely with the central Data Science team in Atlanta to align modeling standards and deployment practices.

• Implemented large-scale fuzzy text matching for millions of automotive part numbers in a Hadoop environment using MapReduce (Java) with Spark backend.
• Developed and deployed machine learning solutions across multiple retail use cases, including:
  • Automotive parts forecasting (including cold-start items)
  • Seasonal product forecasting (e.g., pools)
  • Related item identification
  • Lifecycle analysis
  • Market basket analysis
• Built models primarily in IBM SPSS and Python using Random Forest, XGBoost, and time-series methods (SARIMA, Holt-Winters).
• Provided technical leadership for enterprise store-network initiatives such as sales forecasting, lost-sales estimation, space optimization, and price optimization.
• Designed end-to-end technical architectures, including object-oriented Python frameworks, PySpark project structures (SparkML), and distributed ML optimization tools using DASK and Docker.
• Managed Agile delivery processes (SCRUM in JIRA), coordinated cross-functional stakeholders, and reported progress to VP and SVP-level leadership.