About us

Founded in June 2018 by three colleagues from a major French bank, Chew Data is the result of their desire to get active and transform the world around us.

Our fintech was born following our success during an internal challenge. It is now in full expansion.

Our ambition is to transform the tools of data analysis and financial certification to bring a competitive advantage to our customers.

Because big projects always start with a first step, our philosophy « Start small think big ! » constantly guides us in our work to have a concrete approach at every moment.

Start small think big !
The team


Co-Founder – Technical Manager

In addition to his mastery of software engineering, Adel has acquired a strong experience in data engineering in the field of mobile telephony and investment banking. He has carried out data warehouse implementation projects, predictive analysis, master data management and big data projects.

It is today as a technical manager that he shares his expertise.

Éléonore DE GASTÉ.

Co-founder – Machine Learning Manager

Eléonore discovered finance on the front office (trading) in a cross-asset financial engineer team. Her engineering curriculum specialized in mathematics and her curiosity quickly turned her towards data science.

After working in the team in charge of calculating market risks, she decided to embark on an intrapreneurial adventure to fully exploit her skills.


Co-Founder – Product Manager

Lucile started working as a developer (MOA). Passionate about counterparty risks, she then became a financial analyst on regulatory capital metric (KVA).

Today, with Chew Data, Lucile wants to be a key player in the transformation of the analyst profession.

Mohamed SOUGUIR.

Senior .Net Full Stack Developer

Mohamed started his career as a developer. After 9 years of experience, he has acquired expertise in the field of brokerage in marine insurance and in finance.

Mohamed joined Chew Data to develop the interfaces and APIs of our tools and to enable our users to make the most of the efficiency of machine learning.