Damien Soukhavong
Data Scientist, Data Architecture & Innovation Lead
France
Summary
🤩
Rockstar🎓
Top SchoolDamien Soukhavong is a data scientist and data architecture & innovation lead with 11 years of experience turning analytics constraints into actionable insights across enterprise and freelance contexts. Based in France, he leads data strategy at Le Groupe La Poste while consulting and training through his freelance practice, blending hands-on modeling with architecture and education. An active open-source contributor, he maintains the R interface for the high-profile LightGBM project, improving usability for R users and fixing tricky categorical and compilation issues. He earned a top-three ranking in a national data science challenge in 2017 and pairs business-focused degrees with certifications from institutions including SKEMA and Harvard Graduate School of Education. Known among peers as a “data witch/magician,” he brings a creative, pragmatic approach to making the most of imperfect data and tooling.
11 years of coding experience
Diplôme de Comptabilité et de Gestion, Triple Degree, Diplôme de Comptabilité et de Gestion, Triple Degree at ENOESCertification, Certification at Harvard University Graduate School of EducationHigh School Diploma in Sciences, High School Diploma in Sciences at Lycée Emily BrontëMaster’s Degree, Master’s Degree at SKEMA Business School
French, English, German
Github Skills (8)
decision-tree10
machine-learning10
r-package10
lightgbm10
r10
gradient-boosting10
data-mining9
gbm9
Programming languages (7)
Github contributions (5)
microsoft/LightGBM
Jan 2017 - May 2020
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
Role in this project:
Back-end Developer & R Package Maintainer
Back-end Developer & R Package MaintainerContributions:2 reviews, 86 commits, 114 PRs in 3 years 4 months
Contributions summary:Damien primarily contributed to the R package within the LightGBM repository, focusing on improvements and bug fixes. Their work included addressing compilation errors, resolving issues related to categorical features, and enhancing error messages for end-users. They also added examples and features like adding multiclass training examples, and introducing the `$raw` attribute to the `lgb.Booster` model, improving functionality and usability for R users.
kagglepythondata-mininglightgbmmicrosoft
Laurae2/Bosch
Sep 2016 - Nov 2016
Contributions:387 commits, 1 PR, 3 pushes in 1 month
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