Constant Bridon

I am strongly interested in the creation of value out of data, and we are currently experiencing one of the most satisfying mind shifts of the past 10 years. Companies start to realize that data is not a useless expense to build on, but a real opportunity to assess their results, find insights in their process failures, reclaim their expertise and probably evolve to a more sustainable business. I want to help those who believe in such a potential by accelerating their transition toward a data driven company. Today, it not sufficient to drive a Proof of Concept (POC) to generate value for a customer anymore. Anyone is now able to open a Jupyter Notebook or Python scripts, run a Random Forest on cold data formatted in CSV, and get a Kaggle-like prediction that is no use in a day to day business. The real issue deals with going live with a predictive algorithm : how do I plug it in an existing infrastructure ? How do I schedule, automate, monitor, improve it ? How can I make it usable for the business side ? In order to address these new problematics, I focus on mastering every skill of a complete Data Geek :

architecture expertise : distributed or not, hadoop is not always the answer to store your data/Let me launch this CUDA job on your GPUs/ I can do it 30 times faster in Spark

data science mastering : SVM is interesting but extreme gradient boosting is more performant/MSE is a better metric for your use case regarding the distribution of the target/ deep learning is not only for photo recognition

customer and business understanding : UPLIFT is a metric that provides a ROI for your marketing campaign/No need to use all the data, the performance versus the computation cost is exponential/Superset is a great open source tool to publish the results on a customizable dashboard

I have been working for more than a year for OCTO Technology in the best Big Data team in France. I am an expert in the industry sector and I work on several types of mission, ranging from predictive maintenance of production site, to prediction of critical KPIs in video games, via real time monitoring of manufacturing devices. I am to present my work in predictive maintenance at ENBIS Spring meeting next may. Prior to joining OCTO, I was working as a researcher in data61 (formerly known as NICTA), the best research institute in ICT in Australia on applying Machine Learning to profile GUI users and provide the best amount of information to help them make a decision based on a machine learning prediction. I published my work in two major conferences : CHI WIP 2015 and OzCHI '15.