Employee Portraits – Ivan Capin, Data Scientist
“I’m Ivan Capin and I’m a data scientist at the Crédit Agricole CIB Information Systems Department.”
What does your job involve?
Ivan Capin: Data scientists are responsible for managing, analysing and using data to optimise a company’s products and services.
I’m currently working on a project to redesign the IT tools used to manage risk calculations at Crédit Agricole CIB. This is an extremely sensitive issue as the reports we produce are sent to institutions such as the European Central Bank. It is also extremely complex, as we work with a substantial volume of data, i.e. big data.
I also contribute to proof of concepts (PoCs)* and cross-functional communities aimed at testing and validating the application of new technologies in finance, including the blockchain, artificial intelligence and quantum computing.
* PoCs are trials carried out to test one or more solutions and demonstrate feasibility.
How did you become a data scientist?
Ivan C.: My career has been somewhat atypical. After studying mathematics and cognitive sciences, I worked in video games and then in finance for 15 years, as an IT developer and project manager specialised in algorithmic trading. I then took a sabbatical year to obtain my Master diploma in Big Data and returned to Crédit Agricole CIB to work on big data projects as a data scientist.
What are the career evolution prospects for data scientists?
Ivan C.: Depending on your skills and the opportunities, you can evolve towards management or expert positions (at IT or another department) that are increasingly prestigious and in demand.
Do you need special skills to be a data scientist?
Ivan C.: Data scientists definitely need extremely solid “baggage” in mathematics and programming. At the Bank, the profession closest to data scientist is IT quant.
Can you describe a regular day?
Ivan C.: We work in agile mode, so I’ll tell you more about typical iterations than regular days. We work in three-week iterations consisting of periods of planning, analysis and dialogue with various business lines, as well as solution testing and the initiation of production.
Which technologies do you use to handle this complexity?
Ivan C.: We ingest several terabytes a week in a Hadoop data lake. The data comes from our front-office systems (used by traders) and our calculation grids via Kafka. Decisional analysis is carried out in OLAP cubes and data visualisation in Tableau or UIs developed on a bespoke basis. Continuous implementation is performed with Kubernetes and some production launches are carried out just like at Google!
Do you get to dialogue with other experts?
Ivan C.: Along with increasingly pinpointed training courses, we have several communities for informing ourselves, talking with experts and keeping abreast of the use of new technologies at the Group. For example, we have communities on artificial intelligence, machine learning, smart data, the blockchain, quantum computing and Dataiku Power BI.
What would you say to a student looking to join Crédit Agricole CIB as a data scientist?
Ivan C.: At Crédit Agricole CIB, we have strategic projects harnessing leading-edge technologies for processing financial data. It is an ideal environment for a young data scientist or big data engineer.
Check out all our job offers on our careers website. Stay tuned for more news from the data science communities at Crédit Agricole CIB!