Discover how to build and scale a data team, implement practical machine learning, and drive success with data-driven decision-making in a growing company.

Drawing from my experience as Engineering Manager at incident.io, where I was part of a product team that built the company’s first AI-powered features, this talk combines personal insights and broader leadership strategies.

In this short talk, Lawrence will reflect on his experience leading teams through multi-year migration projects.

In this talk, we’ll discuss how Vercel transitioned to an AI-native company and how you too can operate highly effective, AI-native product engineering teams, from the tools used to the way to stay organised amid the rapidly changing pace of AI.

In this talk, we’re going to explore a framework for evaluating the return on investment (ROI) of complex tech decisions, illustrated with real-world examples that highlight both the traps to avoid and the paths to success. I’ll share methods for pinpointing key metrics that matter, and how to design experiments or proof-of-concepts to measure ROI. Finally we will discuss the importance of staying objective and adaptable throughout the process. 

This talk gives an overview of Data Science and delves deep into the pipeline data scientists use – right from fetching the data, the Python tools and frameworks used to creating models, gaining insights and telling a story.

The talk centres around the benefits of building such a tool, such as transparency, alignment and faster onboarding.

Tech Debt is a natural by-product of software engineering, yet we, in the software industry, don’t attack it with the same excitement or fervor as we do with product innovation or feature development.