Felipe Moraes
I am a machine learning scientist at Booking.com working on personalized discounts under budget constraints.
I have a PhD in Computer Science from the Delft University of Technology. During my PhD, I interned as an applied scientist at Amazon Alexa Shopping, where I worked on finding proxies for what customers find relevant when comparing products during their search shopping journey in order to empower Amazon recommendation systems. Before that I obtained a BSc and MSc in Computer Science from the Federal University of Minas Gerais, visited research labs at NYU and the University of Quebec, and worked as a software engineer intern in a news recommendation system start up.
Sessions
Optimizing machine learning models using regular metrics is a common practice in the industry. However, aligning model optimization with business metrics is closely tied to the objectives of the business and is highly valued by product managers and other stakeholders. This talk delves into the process of training machine learning models based on business metrics in order to enhance economic outcomes. With a primary focus on data scientists and machine learning practitioners, this talk explores techniques, methodologies, and real-world applications that harness the power of business metrics to propel machine learning models and foster business success. We will present a specific case study that demonstrates how we utilized business metrics at Booking.com that brought significant impact on model performance on business outcomes. Specifically, we will discuss our approaches to leveraging business metrics for hyperparameter tuning and reducing model complexity, which instill greater confidence within our team when deploying improved models to production.