Data Scientist
Current▪ Developed Machine-Learning Fraud Detection models.▪ Key contributor to the construction of an operational ML Fairness assessment framework. Presented the subject to varying audiences numerous times, including at internal large-scale seminars.▪ Developed a ML Fairness Python module (+2 000 lines of code) to be used internally by all Scoring Center Data Scientists and analysts. Ensured its adherence to high-quality code standards (Unit Testing, SonarQube...) prior to putting it into production. Prepared and carried out training workshops.▪ Worked on several Innovation topics.▪ Made a Python Plotly data visualization tutorial.▪ Active member of the Scoring Center’s Team Life task force.