Wesley Boelrijk
Wesley is a Senior Data Scientist at KLM Royal Dutch Airlines, where he develops next-generation demand forecasting models with a strong focus on time series. Previously, he led a Machine Learning traineeship at Xccelerated (Xebia), mentoring engineers and scientists through tutoring and live coding, alongside various consultancy projects.
A three-time PyData speaker, Wesley is passionate about Bayesian modeling and making forecasting techniques practical. He holds master’s degrees in Econometrics (VU Amsterdam) and Engineering & Policy Analysis (TU Delft), and a bachelor’s in Aviation Engineering.
Outside of work, he is a devoted Formula 1 fan, fascinated by the sport’s rich data streams and the opportunities they create for advanced modeling. He has attended Grands Prix in Zandvoort, Spa, and Spielberg.
Session
Forecasting time series can be messy, data is often missing, noisy, or full of structural changes like holidays, outliers, or evolving patterns. This talk shows how to build interpretable time series decomposition models using PyMC, a modern probabilistic programming library.
We’ll break time series into trend, seasonality, and noise components using engineered time features (e.g., Fourier and Radial Basis Functions). You’ll also learn how to model correlated series using hierarchical priors, letting multiple time series "learn from each other." As a case study, we’ll analyze Formula 1 lap time data to compare drivers and explore performance consistency using Bayesian posteriors.
This is a hands-on, code-first talk for data scientists, ML engineers, and researchers curious about Bayesian modeling (or Formula 1). Familiarity with Python and basic statistics is helpful, but no deep knowledge of Bayes is required.