About Aurélia
English
Fluent
Italian
Native or bilingual
French
Native or bilingual
Spanish
Fluent
Portuguese
Fluent
Experience
- CEVA LogisticsDataiku platform managerLOGISTICS AND SUPPLY CHAINJanuary 2023 - Today (3 years and 5 months)Since 2023, I have held the role of Dataiku Platform Manager / Dataiku Expert for the global CEVA platform.I restructured the Design / UAT / Prod architecture, implemented governance, standards, security, flow zones, and industrialized pipelines.I managed the on-prem → AWS migration, optimized performance (SQL, Spark, S3, PostgreSQL, containers), and implemented advanced monitoring (usage, performance, costs).I manage over 20 Dataiku projects (BI, Data Engineering, ML, APIs), while coordinating the Cloud, APS, DBA, Network, and Security teams.Result: a stable, optimized, and scalable platform, with significant cost reduction and faster delivery.Malt keywords:Dataiku • Platform Manager • Data Engineering • AWS • Architecture • Cloud Migration • Performance Optimization • Governance • Monitoring • SQL • Spark • S3 • PostgreSQL • Pipelines • Dev/UAT/Prod • Flow Zones • Automation • CI/CD • ML/AI • Scalability • Cost Optimization
- Biogen GmbHData manager / analystPHARMACEUTICALS INDUSTRYApril 2022 - July 2022 (3 months)Paris, FranceAt Biogen, a pharmaceutical company, I contributed to data analysis and the creation of dashboards and alerts on Dataiku. The project involved setting up an application to measure the evolution of different neurological diseases across a large number of studies worldwide. My objective was to understand the needs of different business units and studies, to ensure analysis reports that would control not only data quality but also patient adherence.Stack: Dataiku, Python, AWS
- Frigo magicData scientistTECHOctober 2021 - January 2022 (4 months)Rennes, FranceAt Frigo Magic, I worked on understanding customer behavior to improve user loyalty for the application. I used various models to understand e-personas, identify churn causes, and propose tailored solutions for different user groups. I employed models such as regressions, random forest, and classification (primarily K-means).I also performed sentiment analysis of users to understand areas for application improvement through PlayStore comments (scrapping and NLP).The results were presented as dashboards and reports.This study led to measures that increased the number of users and, more importantly, boosted the retention rate by +71%.Stack: R, PowerBI
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Education
- Master of Quantitative Measurements and EconometricsUniversity of Marseille2020
- MBA in Business Strategy and Digital TransformationESG2018
Certifications
- Machine LearningCoursera - Stanford University2021
- Neural Networks and Deep LearningDeepLearning.AI2021