Bader Dammak
Senior Machine Learning Engineer at Blue Bridge Group AI
Paris, Ile-de-France
Summary
👤
Senior🎓
Top SchoolBader Dammak is a Senior Machine Learning Engineer based in Paris with eight years of experience building production ML systems, from document understanding APIs to medical image augmentation using GANs. He combines strong academic training in applied mathematics and data science at École Polytechnique and ENSTA with hands-on engineering—implementing models from scratch in TensorFlow and designing scalable ML pipelines for clients. An active contributor to streaming-ML open source (scikit-multiflow and River), he authored and tested synthetic data generators used to simulate concept drift, reflecting deep expertise in online learning and data-stream benchmarking. At Datakeen he led end-to-end AI projects and deployed an API serving 20 clients, and his work at Therapixel improved mammography augmentation for cancer detection. Known for bridging rigorous research with pragmatic product delivery, he often focuses on synthetic-data strategies to make models robust in real-world, evolving environments.
8 years of coding experience
6 years of employment as a software developer
Engineer's degree Applied Mathematics, Engineer's degree Applied Mathematics at Ecole Nationale d'Ingénieurs de TunisMP: Mathematics and Physics, MP: Mathematics and Physics at IPEIT - Institut Préparatoire aux Etudes d'Ingénieurs de TunisMaster (M2) Applied Mathematics / Data Science Applied Mathematics, Master (M2) Applied Mathematics / Data Science Applied Mathematics at École PolytechniqueEngineer's degree Applied Mathematics, Engineer's degree Applied Mathematics at ENSTA
English, French, Arabic
Stackoverflow
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Github Skills (16)
data-stream10
data-generation10
numpy10
incremental-learning10
data-streams10
machine-learning10
python10
unit-test10
streaming10
streaming-data10
elearning10
data-science9
online-machine-learning9
unit-testing9
generator8
Programming languages (1)
Github contributions (5)
scikit-multiflow/scikit-multiflow
May 2018 - May 2019
A machine learning package for streaming data in Python. The other ancestor of River.
Role in this project:
Data Scientist
Data ScientistContributions:60 commits, 1 PR, 35 pushes in 11 months
Contributions summary:Bader implemented and tested a sine wave generator for streaming data, including options for classification function selection, class balancing, and noise addition. They also added a test suite to validate the sine generator, verifying various properties and behavior. Furthermore, the user contributed to adding new data generators to the repository, specifically, AGRAWAL and Hyperplane generators, along with their test files. The user's work focuses on expanding the library of synthetic data streams.
moapythonstreaming-datariverancestor
online-ml/river
May 2018 - May 2019
🌊 Online machine learning in Python
Role in this project:Data Scientist
Data ScientistContributions:60 commits in 11 months
Contributions summary:Bader primarily contributed to the development and testing of a sine generator, a data stream used to simulate abrupt concept drift. Their work included implementing the sine generator and creating a test suite, demonstrating an understanding of data stream generation techniques and testing methodologies. The contributions also included adding support for the AGRAWAL and Hyperplane Generators with tests and dataset stream generators. Additional commits showed code related to support for dataset to stream generator and standardization of the stream class.
stream-processingpythonstreaming-datadata-scienceonline-statistics
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