Speaking & Advisory
I focus on data science leadership, the operating foundations behind reliable AI delivery, and how organizations translate models into decisions. These sessions are suited for leadership teams, conferences, podcasts, and internal strategy discussions.
A mix of upcoming appearances and selected talks that show where I am speaking publicly now and the kinds of audiences I have spoken to before.
Joining Toronto practitioners for the first Databricks Toronto User Group meetup of 2026 to discuss what is working, and what is not, when teams are driving business outcomes with Databricks.
The event runs from 5-9 PM ET and brings together speakers from Paystone, the Ontario Securities Commission, and Sobeys.
A career-focused talk on using AI, machine learning, and big data to solve real retail problems, drawing a line from technical work to business outcomes.
Presented for a university audience as a practical look at how data science work translates into applied decision-making in the retail industry.
A practical discussion on how organizations can move from isolated ML experiments to scalable, production-grade AI systems. This talk focuses on architecture, automation, and operational discipline required to support high-volume use cases across multiple channels.
Themes include platform design, MLOps and reliability, cost control, experimentation at scale, and turning data science into a repeatable delivery engine rather than a collection of projects.
Many organizations build strong models but struggle to translate predictions into measurable outcomes. This session explores how to design decision frameworks and automated processes that connect models to execution.
Topics include decision layers, integration with business workflows, experimentation design, measurement rigor, and closing the loop between analytics and action.
Adopting AI, especially more autonomous or agentic systems, requires more than technical capability. This talk focuses on leadership, governance, and organizational alignment needed to introduce AI responsibly.
Key themes include change management, cross-functional trust, ethical considerations, customer impact, risk mitigation, and building accountability into AI systems from the start.