Freelancer profile translated to English.
Back to original languageAbout Charlotte
With 8 years of experience in Data Science, including 2 years dedicated to MLOps, I am positioned at the intersection of statistics and computer science. I have developed strong expertise in cloud environments, particularly on AWS and Google Cloud. Thanks to my MLOps-oriented work philosophy, I am effective in daily Machine Learning Engineer tasks. I specialize in using Dataiku (for 5 years) for data acquisition, automation and optimization of analytical pipelines, as well as their deployment in production.
I am also proficient in English, both spoken and written, which allows me to collaborate effectively in international environments and successfully lead projects with diverse teams.
Finally, in terms of Data Visualization, I have acquired solid expertise in using Tableau and Power BI to create interactive dashboards, analyze complex datasets, and provide intuitive visualizations. These tools enable me to transform raw data into relevant visual insights, thereby facilitating decision-making for stakeholders.
One of my additional motivations lies in the industrialization of machine learning solutions. By combining technical expertise and pragmatism, I am capable of delivering reliable solutions, aligned with business needs and optimized for production.
I am also a co-host of the @DataKdemy Podcast 🎙️🎧
French
Native or bilingual
English
Fluent
Remote only
Primarily works remotely
Experience
- L'Oréal (AE)ML Engineer - Stock ForecastingFASHION AND COSMETICSOctober 2023 - February 2024 (5 months)Paris, FranceContext: Industrialization of a stock forecasting system over an 18-month horizon, aimed at optimizing global planning, production, and logistics.Tasks performed:
- Deployment of a forecasting algorithm (time series models, regression, optimization) developed in Python and adapted to the operational constraints of Supply Chain Planning.
- Industrialization and automation of the ML chain:
- Containerization of ML modules with Docker,- Deployment on a Kubernetes architecture (GKE) for scalable and robust execution,- Implementation of orchestrated pipelines (batch + monitoring).- Model lifecycle management: performance monitoring, periodic recalibration, drift management (data drift / model drift).
- Close collaboration with Data, Supply, and IT teams to integrate the model into internal systems and business workflows.- Creation of Power BI dashboards highlighting forecasts, discrepancies, trends, and logistics performance indicators.- Comprehensive technical documentation: architecture, data pipeline, deployment procedures, user guide.
Results achieved:Reduction of overstock by ~15% through better volume anticipation and forecast stabilization.Technologies:Python, Git, Power BI, GCP (GKE, BigQuery), Kubernetes, Docker, ML monitoring tools. - BNP ParibasData Scientist - Credit Risk - Dataiku ReferentBANKING AND INSURANCEFebruary 2024 - Today (2 years and 4 months)Paris, FranceContext:Development, automation, and industrialization of internal scoring, data quality, and regulatory reporting solutions within the BCEF Credit Risk team.Tasks performed:
- Development of an internal RWA scoring algorithm in Python: model design, feature engineering, optimization, and complete integration into Dataiku for automated execution.
- Deployment of RWA scoring on Dataiku's Automation Node, managing dependencies and run scheduling.
- Contribution to ESG initiatives: integration of environmental, social, and governance indicators into risk pipelines, enrichment of exposures via APIs and internal/external sources.
- Advanced workflow automation (Python, SQL, Dataiku): creation of Python plugins, development of custom triggers to orchestrate scenarios and automatically launch monthly and quarterly reports.
- Implementation of data quality controls (consistency, completeness, anomalies) and in-depth analysis of exposures (PD, LGD, EAD, RWA).
- Preparation and industrialization of reports for production deployment.
- Creation of regulatory reporting dashboards in Tableau (Desktop & Server).
- Monitoring of team Teradata datalabs.
- Migration of VBA Macros and Excel reports to Dataiku.
- Member of the internal BNP Dataiku community.
- Mentoring a Data Scientist intern.
- Writing documentation and training business teams on Dataiku and Tableau tools.
Results achieved:• Improvement in the accuracy and consistency of RWA and ESG reporting.• Significant reduction in manual effort through automation (monthly and quarterly time savings).Technologies:Python, SQL, Git, Tableau Desktop, Server, Dataiku, Teradata, Jira, Confluence - THALES LAS FranceData Scientist/Analyst - Monitoring and Calibration of Predictive Maintenance AlgorithmDEFENSE AND MILITARYNovember 2022 - October 2023 (11 months)Élancourt, FranceContext:Design and deployment of a complete analytical pipeline for predictive maintenance.Tasks performed:
- Creation of an end-to-end analytical pipeline.
- Feature selection and preparation with Python.
- Automation of the data pipeline (unit tests, integration, controls, metrics).
- Versioning of data and models with Git.
- Creation of monitoring dashboards (Bokeh).
- Implementation of control tests and anomaly detection.
- Calibration of predictive maintenance algorithms.
- Automated deployment via Automation.
- Documentation.
Results achieved:Automation and optimization of processes reduced team work time from 7 days to just 2 days.Technologies:Dataiku, Python (OOP), SQL, Git, Bokeh, Splunk, GitLab CICD, Linux, Docker
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Education
- Doctorate in Applied Mathematics StatisticsUniversity of Limoges2019
- Master's in Statistics and Data ProcessingUniversity of Clermont-Ferrand2016