Walid Benbihi
Consultant Data Science
Greater Paris Metropolitan Region France
Summary
👤
Senior🎓
Top SchoolWalid Benbihi is a Consultant Data Scientist with nine years of experience building machine learning and computer vision solutions for startups, SMEs and CAC40 clients across retail, telecom, aid agencies and defence-related imagery analysis. He combines an engineering background from École Centrale and an MSc in Machine Learning from Imperial College with hands-on expertise in cloud architectures, GDPR-aware data pipelines and production ML—helping clients like L'Oréal, Danone and BPI France translate data into measurable business impact. As a former founder and open-source contributor, he has implemented real-time deep learning systems (notably a TensorFlow Stacked Hourglass pose‑estimation project) and led teams delivering demand forecasting, campaign optimization and satellite-based damage assessment in conflict zones. Pragmatic and product-minded, he sits at the intersection of technical depth and strategy, able to shepherd projects from research prototypes to scalable, compliant deployments.
9 years of coding experience
3 years of employment as a software developer
Diplôme d'ingénieur, Maths, Diplôme d'ingénieur, Maths at Ecole Centrale ParisMSc, Computing - Machine Learning, MSc, Computing - Machine Learning at Imperial College LondonLicence, Mechanical Engineering, High Honors: Top 5%, Licence, Mechanical Engineering, High Honors: Top 5% at Université Pierre et Marie Curie (Paris VI)
English, Arabic, Spanish, French
Github Skills (7)
machine-learning10
tensorflow10
data-processing9
python9
mask-rcnn9
faster-rcnn9
back-end-development7
Programming languages (4)
Github contributions (5)
wbenbihi/hourglasstensorflow
May 2017 - Sep 2022
Tensorflow implementation of Stacked Hourglass Networks for Human Pose Estimation
Role in this project:
Back-end Developer and ML Engineer
Back-end Developer and ML EngineerContributions:1 release, 353 commits, 10 PRs in 5 years 4 months
Contributions summary:Walid appears to have focused on the core functionality of the Stacked Hourglass Networks for Human Pose Estimation implementation using TensorFlow. This is suggested by changes to the `trainer.py`, `hourglassModel.py`, and `layers.py` files, along with parameter adjustments and the addition of data generation code which points towards ML model training. The user's commits included modifications to model architecture, suggesting active development on the underlying deep learning model for pose estimation. The edits also focused on implementing data generation for the model with the development of the `datagen.py` file.
human-pose-estimationtensorflowpose-estimationstacked-hourglass-networksnewell
EpidIA-org/app_data
Apr 2020 - Jun 2020
Data Collection & Automation streams
Contributions:33 commits, 6 PRs, 22 pushes in 2 months
automation
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