Dileep Menon is a Data Science Manager at Booking.com with a decade of experience applying experimentation, causal inference, and predictive modeling to performance marketing. He combines hands-on A/B testing and reporting with strategic insight generation to optimize SEM outcomes across large-scale digital campaigns. Holding a PhD in mathematics from UIUC and an MASt from Cambridge, he brings rigorous probabilistic and statistical foundations to practical business problems. His background includes producing research-level proofs and teaching complex math, which informs a clear, analytical approach to communicating results and mentoring teams. Beyond industry work, he contributes to open-source scientific computing—implementing shallow water equations and improving code quality in the pysph project—showing an appetite for numerical methods and reproducible code. Based in Amsterdam, he blends deep theory with production ML and experimentation to turn causal questions into measurable business impact.
10 years of coding experience
9 years of employment as a software developer
Master of Advanced Study - MAst., Pure Mathematics, Master of Advanced Study - MAst., Pure Mathematics at University of Cambridge
University of Illinois Urbana-Champaign
Bachelor of Science - BS with Honors, Mathematics, Bachelor of Science - BS with Honors, Mathematics at The University of Chicago
A framework for Smoothed Particle Hydrodynamics in Python
Role in this project:
Back-end Developer
Contributions:12 commits, 1 PR, 3 comments in 3 months
Contributions summary:Dileep contributed significantly to the `pysph` repository, focusing on the shallow water equations module. Their work involved implementing equations for solving shallow water problems and updating the particle array. They also added various equations and classes related to shallow water flows. Furthermore, the user refactored code and fixed Pep8 issues, improving the code quality and readability. The user also added an example for the Okushiri Tsunami.
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