Kaggle competitions demand more than just machine learning expertise; they require strong coding and organizational skills. The author recently earned a Silver Medal in the NeurIPS – Ariel Data Challenge 2025 and reflects on the lessons learned about organization in data science projects. Key insights are centered on the importance of a well-structured codebase, efficient experiment tracking, and clear documentation. Poor organization can lead to wasted time and irreproducible results, as seen in a referenced case where a multi-million dollar project failed due to a code bug that went undetected. The author emphasizes the need for a thoughtful project structure, recommending the Cookiecutter Data Science template as a solid starting point. This template establishes a clear hierarchy for data, models, notebooks, and documentation, which helps maintain clarity throughout the project. The author also suggests using an environment manager like uv, which simplifies package management compared to traditional methods. By following these organizational strategies, data scientists can improve their workflow and reduce the likelihood of errors that compromise project integrity.