Within the Group’s Corporate and Institutional Banking (CIB) arm, Maude Lecluyse works on data...
The banking jobs : Data Scientist Senior
In the era of Big Data, the ability to transform raw data into useful information to solve complex problems becomes a valuable expertise, especially for banks. Magicians, polyglots, conductors ... Who are the Data Scientists? Meeting with Thomas Gilles, who has been a "data magician" for the last three years at BNP Paribas Corporate & Institutional Banking (CIB).
We hear more and more about Data Science. Why are banks so interested in this new discipline?
Banks have always been large producers of data. The purpose of Data Science is to rely on mathematical, statistical, computer and visualization tools to transform this raw data into useful information. It is therefore natural for banks to pay particular attention to them. Today, 80% of the data within banks is unstructured and structured. The role of Data Science is to find ways to identify this unstructured data, classify it in a database and use it best to assist in the bank's activities.
What are your responsibilities as Senior Data Scientist at BNP Paribas?
I have several hats. On the one hand, I am a Product Owner, with the task of coordinating the intervention of different businesses to develop products internally. Usually these are Artificial Intelligence or Machine Learning projects. I am therefore a coordinator between the internal client, the Data Scientists team and the developers. On the other hand, I am the single point of contact for two internal customers: human resources and BNP Paribas Securities Services. My role is to be their main advisor on all their Data Science topics.
Photo : Thomas Gilles
What does a project run by a Data Scientist look like?
I am currently working on an Artificial Intelligence Project whose purpose is to extract automatically unstructured data from documents such as fax, invoices and other contracts. I am very proud of this since after more than two years of development we have succeeded in creating a tool that is more effective than similar solutions available on the market. Another example: I collaborate with the Human Resources Department on a project related to Machine Learning and analytical data.We are developing a tool that will enable us to have a vision of current and future skills needs. This allows HR to anticipate recruitment over the next few years and to ensure that the bank does not lack experts, especially in digital areas.
after more than two years of development we have succeeded in creating a tool that is more effective than similar solutions available on the market.
You started your career in finance. How did you get interested in Data Science?
During my studies, I had a double major in applied mathematics to financial markets and business law. I first worked in the trading rooms of a large American bank, before joining a consulting firm where I developed an appetite for Big Data and analytics. Through this non-typical path, I have acquired three essential skills for my job: knowledge of the banking environment, project management and computer science expertise. This allows me to understand fully the needs of my internal clients, but also to solve technical problems with data scientists or developers. Being able to work with various stakeholders in throughout the bank is key to run a project and deliver it on time and in budget.
We are facilitators and our ultimate goal is to simplify the lives of employees.
What are the other major challenges of your job?
I have to show great intellectual agility to juggle with different expertise: project management, mathematics, computer development...
I have to show great intellectual agility to juggle with different expertise: project management, mathematics, computer development... I also need to master these topics well enough to get my counterparts out of their comfort zone. At the same time, I must ensure that we do not develop solutions that are too complex, as they would tend to slow end-to-end processes. We are facilitators and our ultimate goal is to simplify the lives of employees. This is also very satisfying when removing a thorn from a client's foot or, for example, freeing up people’s time so that they can spend more time on tasks with higher added value.
How will Big Data revolutionize the banking industry in the next few years?
In my opinion, Big Data will lead to a commoditization, with increasing competition between the various players in the banking industry. Data Scientists will be key in helping banks differentiate themselves by developing increasingly innovative solutions.
How could you explain your business to a child?
I would say I am a bit like an orchestra conductor. I coordinate the business & functions within the bank to carry out a project together. I give the tempo, maintain the balance between the speakers and ensure that the project moves crescendo until final delivery.
What do you like in your job?
I am always surprised when some of my clients tell me I'm a bit like a magician. Even if we hear about AI and Data Science more and more, many continue to see algorithms as something abstract, almost supernatural... It is quite fascinating, but also very gratifying to see that our work has a great impact on the daily lives of others.
Describe your ideal working day
My recipe for a successful working day: go to work early in the morning, have a nice coffee, take a sporting break during lunchtime, and then find colleagues at the end of the day to celebrate the team's successes.
Credit photo header: ©alice_photo
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