Data science
Big data
Transforming our customers’ data into business intelligence is what inspires us. We are committed to ensuring that decision-making tools are developed in a controlled and dynamic way.

Our Professions & Services
Every day, our Data Scientists work with our clients’ business teams: Risks, Marketing, Digital, Research, and IT.
We have expertise in the following fields:
- Data exploration
- Statistical studies, Predictive modelling
- Machine Learning
- Algorithm design and optimisation
- Data Visualisation

Our Data Scientists are graduates of “Grandes Écoles” and specialised Master’s programmes in Statistics, Mathematics, and Big Data.
They master the data value chain and know how to apply statistical methods and design algorithms.
They know how to share and promote their best practices.
Their work aims to meet the following goals:
- Detecting trends
- Creating value
- Taming and structuring data
- Mutualising and prioritising information
- Maximising information communication
Our Data Scientists implement the following technologies: SAS, R, Python, the Hadoop ecosystem, Tableau, Qlikview, etc.
Our Big Data Engineers will handle:
- formal analysis of business needs
- identifying and collecting data sources
- aggregation, cleaning, and enrichment
- predictive modelling, machine learning
- implementing algorithms (designing and developing data-research and analysis algorithms: PYTHON, HADOOP, HIVE, PIG, NoSQL, HDFS, MapReduce, Elasticsearch,SCALA, SPARK, R…)
- designing applications and services for end users
- recommendation for industrialising processes
Our statisticians specialised in risks provide assistance to Risk-Management Departments implementing regulatory changes, by working in the areas of:
- Project management support and oversight in an SAS environment
- Designing and developing management tools
- Modelling Basel parameters (PD, LGD, EAD, ELBE)
- Back-testing and stress-testing of models
- Calibrating models
- Approving models
- Designing and optimising lending models
- Impacts and changes related to the IFRS 9 standard
- Fraud detection
- Detecting operational risks
We apply analytics to multiple business areas:
- Marketing: optimising targeted marketing, customer segmentation, Q scoring, churn scoring, lifetime value
- Sales performance: Acquisition, up-selling, cross-selling, loyalty, overseeing marketing and sales activity
- Digital: multichannel marketing, optimising the conversion funnel
- IoT: data storage and analysis and decisions on the information derived from objects
- Operational efficiency: managing industrial equipment and optimising the supply, storage, and transport of goods
22
Terabytes of Processed Data
80%
Success rate of customer interviews
100%
Data Addicted
Our Achievement
Identifying the sources of growth and profitability from internal and external data, then defining action plans to leverage them
- Studies and statistical modelling to respond to business challenges (Risks, Marketing, Digital technologies, Research, etc.)
- POCs and implementation of Big Data projects
- Support in choosing Data Visualisation tools and designing management indicators
- Basel and Credit Risk Project Management Support