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...).
- Worked on several Innovation topics.
- Made a Python Plotly data visualization tutorial.
- Active member of the Scoring Center’s Team Life task force.