Amir Tavasoli

• Led all Advanced Analytics initiatives within the Marketing organization, expanding my scope from Data Science Manager to enterprise-wide AI leadership.
• Built and scaled AI, ML, and Agentic AI solutions that directly supported marketing strategy, including:
  • One-to-one personalization
  • Generative AI use cases
  • Retail media measurement
  • Marketing Mix Modeling (MMM)
  • Churn prediction
  • Personalized pricing
  • Customer life stage management
  • eCommerce activation
• Designed and architected end-to-end AI systems in partnership with cross-functional teams, ensuring alignment between technical architecture and business objectives.
• Owned the full lifecycle of solutions, from data ingestion and modeling to deployment, experimentation, and executive decision support, ensuring scalability, reliability, and measurable impact.
• Led development of two generative AI agents:
  • An automated, context-aware marketing offer generation and personalization agent
  • An AI decision layer over MMM outputs to optimize channel allocation and marketing investment
• Implemented a production-grade MMM framework integrating multi-agency data pipelines and linear mixed models to generate elasticity curves and guide spend optimization.
• Guided Retail Media Network measurement strategy by:
  • Building robust A/B testing pipelines
  • Developing Markov-based multi-touch attribution models
  • Quantifying cross-channel customer interaction impact across owned and third-party platforms
• Enforced rigorous, state-of-the-art measurement methodologies across programs to ensure transparency, accountability, and repeatable business outcomes.

Promoted to Director

• Led a 15-person full-stack ML team, across hybrid and offshore staffing, delivering enterprise personalization across Sobeys banners.
• Oversaw weekly recommendations for 16M Scene+ customers across email (SFMC), web, and mobile platforms.
• Owned the end-to-end pipeline, from Enterprise Data Lake extraction through model development, deployment, and measurement.
• Architected and reviewed scalable ML systems using PySpark and Databricks in Azure while enforcing code quality and production standards.
• Implemented MLOps and reliability practices to improve automation and stability.
• Partnered with Marketing, Loyalty, and Cloud teams, managed agile delivery through JIRA and PlanView, and provided 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.

Promoted to Technical Lead, Jun 2021

• Built and maintained scalable ML systems for personalized recommendations, in-app advertising, and marketing optimization across hundreds of millions of users.
• Developed production-grade models using Scala and Python with AWS-based infrastructure.
• Designed and maintained data engineering pipelines to clean and transform large-scale application log data into ML-ready features.
• Created executive dashboards and visualizations supported by Kubeflow-based ML workflows.
• Operated in a dual role as both Machine Learning Engineer and team lead within the core ML team.
• Managed and mentored a team of 6 MLEs, translating business requirements into technical roadmaps and delivery plans.
• Led agile planning through JIRA, coordinated cross-functional stakeholders, and produced executive-level progress and impact reports.

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

Previously Senior Modeling Analyst (Sep 2014 – Mar 2018) and Assortment Analyst (Jun 2013 – Sep 2014), Automotive Data Science Team

• 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.