How can data science help young athletes navigate their careers? In this talk, I’ll share my experience building a career path planner for aspiring ice hockey players. The project combines player performance data, career path patterns, and predictive modeling to suggest possible development paths and milestones. Along the way, I’ll discuss the challenges of messy sports data and communicating insights in a way that resonates with non-technical users like coaches, parents, and players.

When we at Bol decided to personalize campaign banners, we did what many companies do: bought an expensive solution. As a software engineering team with zero data science experience, we integrated a third-party recommender system for €1 million annually, built the cloud infrastructure, and waited for results. After our first season, the data told a harsh truth—the third-party tool wasn't delivering value proportional to its cost. We faced a crossroads: accept mediocrity or build our own solution from scratch, tailored to our requirements and architecture. We'll walk you through our journey of building a more intelligent and flexible recommendation system from the ground up, and how this journey saved us over a million euros per year. We will share the incremental steps that shaped our journey, alongside the valuable lessons learned along the way