Mohamed Amine Jebari
Mohamed Amine Jebari is a Lead Data Scientist based in Berlin, specializing in large-scale machine learning systems, Marketing Mix Modeling, and applied NLP. With extensive hands-on experience in Python and the scientific ecosystem, including pandas, NumPy, scikit-learn, PyMC, transformers, and Hugging Face. Amine builds end-to-end solutions that bridge rigorous statistical modeling with modern LLM-driven workflows.
Working at a data-driven consultancy, he leads a team of data scientists while remaining deeply involved in technical development, from Bayesian modeling to production-grade pipelines on AWS. Their work often focuses on solving real-world business problems with interpretable, high-impact models.
Curious to uncover the truth and being a big fan of puzzles, he is now heavily working on causal inference and marketing mix models, pulling one inch at a time, closer the the truth.
Session
In every marketing project, teams strive to find more data, a longer timeframe, and more detailed splits, just to fix noisy channel attribution.
But what if structure played a bigger role than size and volume?
In this talk, we try to prove this. Using a simple toolkit like Arviz and PyMC, we show you a simple hierarchical mix model, and how, by applying partial pooling, we can stabilize important KPIS like ROAS estimates across sparse channels- without the need for more data.
We will go through the code, transformation, and the real-life practices that allow us to get as close to the truth, to be able to have a meaningful impact in the marketing world.
The approach will be centered around marketing mix models, different transformations, and how useful it will be for the business.