Guillaume Bokiau
Senior Financial Analyst at Johnson & Johnson Innovative Medicine
Zurich, Zurich, Switzerland
Summary
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SeniorGuillaume Bokiau is a senior financial analyst based in Zurich with 13 years of experience blending finance, data analytics, and technology to drive supply chain and commercial performance. He specialises in first-principles price and profit analysis, commodity risk, and working capital optimisation, translating complex margin attribution into actionable strategic decisions. Guillaume has a track record of practical automation and process redesign—cutting month-end close cycles, standardising cross-regional workflows, and building self-service analytics that unlocked tens of millions in free cash flow and cost savings. He partners effectively across procurement, operations and IT, scaling analytics adoption and turning financial insight into measurable negotiation and sourcing wins. Less obvious: he contributes to open-source data and front-end projects, demonstrating hands-on coding fluency that strengthens his ability to implement analytics solutions end-to-end. Pragmatic and curious, he focuses on solutions that balance rigorous controls with operational speed.
13 years of coding experience
English, French, Dutch, German
Stackoverflow
51reputation
94reached
3answers
0questions
Github Skills (17)
javascript10
probabilistic-programming10
py10
python10
statistics10
responsive-design10
statistic10
mcmc10
bayesian-inference10
numpy9
html9
gaussian-processes8
variational-inference8
theano8
css6
Programming languages (7)
Github contributions (5)
pymc-devs/pymc
Jan 2018 - Jul 2018
Bayesian Modeling and Probabilistic Programming in Python
Role in this project:
Data Scientist
Data ScientistContributions:13 commits, 10 PRs, 64 comments in 5 months
Contributions summary:Guillaume primarily contributed to bug fixes and optimizations within the PyMC3 library, focusing on improving the efficiency and correctness of Bayesian modeling and probabilistic programming functionalities. Their commits addressed issues in core distributions like `MvNormal` and `Multinomial`, ensuring accurate calculations and preventing potential errors. Furthermore, the user simplified timeseries code and made improvements to Gaussian Process (GP) implementations, demonstrating an understanding of the underlying statistical methods and Theano/PyTensor integration.
pythonbayesian-inferencestatistical-inferencemachine-learningprobabilistic-programming
scottjehl/picturefill
Apr 2014 - Apr 2014
A responsive image polyfill for <picture>, srcset, sizes, and more
Role in this project:Front-end Developer
Front-end DeveloperContributions:8 commits in 1 day
Contributions summary:Guillaume primarily contributed to the `picturefill` library by addressing bugs and improving the handling of responsive images. Their work involved fixing issues related to element selection, candidate selection, and applying the best image candidate based on resolution. They also made minor corrections to documentation and indentation. The contributions focused on improving the core functionality of the library for responsive image handling.
srcset-sizesimagepolyfillresponsive-imagespicture
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