💼 Professional experience
Data science Expert Since 2022
- Debt recovery: Development of a tool to automatically score the probability to recover at every step of the process
- Real Estate: Development of a tool to help the client explore new opportunities, from scraping and consolidating internal and external data sources
- Media: Building a model to better predict the attribution of campaigns based on historical data
Data Science Team Lead 2019-2022
- Lead the Data Science team. Responsibilities: Customer segmentation, predictive modeling, dashboarding, advanced analytics and data processing.
- Develop a web-based Data Platform that automate the creation of clients and internal dashboards / reports.
Technologies : R, Shiny, Python, Power BI, SPSS
McKinsey & Company
Data Science Specialist 2016-2019
- Product Owner of a Big Data platform that leverage professional social media information to improve HR processes (CV screening, competition benchmarking, hiring strategy).
- Develop machine learning algorithms in the Human Resources domain and advise client on their Big Data strategy and roadmap.
Technologies: Python, TensorFlow, R, Elastic Search, AWS Glue, AWS Athena, Snowflake, Tableau
Team lead customer analytics 2013-2016
- Lead the customer analytics team. Responsibilities: Customer buying behaviour analysis, Segmentation, Targeted/1to1 marketing, strategic reporting/analysis.
- Customer 360 database owner: Owner of the customer big data strategy and implementation.
- Development of new analytical models:
- Marketing: recommendation engine (next best offer), promotion sensitivity (uplift modelling), price elasticity, customer lifecycle management (loyalty segmentation, loyalisation modelling).
- Procurement: assortment optimization tool.
- Finance: sales forecasting methodology.
Technologies used: R, SAS, Google BigQuery, DB2, Microstrategy
Consultant / Project leader Data Science 2008-2013
- Lead projects involving from 2 to 6 consultants/developers.
- Predictive modelling projects: customer value estimation, lead generation model, churn and up-selling models, marketing campaign optimization, risk models and fraud detection.
Technologies: R, SAS, Rank, SQL Server, Amadea
Sectors: Retail, FMCG, e-Commerce, Banking, Data Brokers, Market Research and Public institutions.
Departments: Marketing & Sales, Finances, HR and Procurement
🦾 Certifications, trainings
- Machine learning/statistics: regression, classification, clustering, time series forecasting, NLP, deep learning (Tensorflow, PyTorch).
Software & programming
- Certified AWS cloud practitioner 2022
- Data programming: R, Python, SAS, SQL, VBA
- Data visualization, reporting: Tableau, Power BI, Microstrategy, Shiny
- Database: SQL Server, DB2, Elasticsearch, Snowflake, AWS-Athena, Redshift, Google-BigQuery
🎓 Academic credentials
- Master in Statistics UCLouvain, 2008
- Master in Sociology UCLouvain, 2007 Magna cum laude
🇫🇷 French: native
🇬🇧 English: professional proficiency
🇳🇱 Dutch: conversational
🔐 Proprietary and confidential. 💡 Profile CVs are provided for illustration purposes. ✅ Planning of specific experts will be secured upon formal agreement.