Machine learning specialist
Datacratic/iPerceptions, Juin 2015 – Today
Responsible for online real-time bidding advertising campaign performance made through Datacratic RTB-Opt product. Making sure campaign managers get the best ROI possible by monitoring machine learning models performance on key metrics (Click-Through Rate, Cost Per Click, Cost Per Acquisition).
Continuously improving the campaign optimization in order to fill campaign managers needs: enable optimization on small campaigns. Improve the system C++ and Python technology stack in order to support new use cases and improve every day operations.
Development of a machine learning pipeline in Golang using Amazon AWS machine learning and Kubernetes to drive the new audience recognition technology.
Machine learning intern
Datacratic, September 2014 – January 2015
Research and development on application of neural networks for online real-time bidding advertising problems. Namely, predict accurately the probability of click by the user given an ad impression.
MILA, University of Montreal, September 2013 – September 2014
Help develop a Python tool to ease comparisons between machine learning models and enable research for new models for the facial landmarks recognition task.
Contributions to Pylearn2, a MILA library to ease machine learning model training workflow, in the form of refactoring the codebase and improve logging.