Global Markets is the part of the Corporate and Institutional Bank (CIB) which deals with all Capital Market trading activities: we are indeed offering millions of different products to our clients across the world on various asset classes like Equity, Foreign Exchange, Interest Rates, Credit or Commodity. Those products can be as simple as trading a stock on an exchange to quite complicated structures meeting the very specific investment needs of a client.
As Market Maker we are constantly offering to buy or sell those products at a price which needs to react quickly to any Market conditions and sentiment change. Our goal is indeed to offer the best-in class service to our client while managing tightly the risk associated to those products.
The role of our Lab in this context is to use the latest techniques of Machine Learning, Artificial Intelligence or Natural Language Processing to help Global Market do a better job.
This means for example understanding better the needs of our clients to provide acute recommendation, help the Sales prioritize better their daily calls to improve their hit rate by being more relevant to their clients, anticipate better the next move in the Market to help trading offering a better service or finally working with Economists and Strategists to analyse better what drive the economy or a particular asset.
For this we need to leverage the gigabytes of data we are accumulating every day, under very structured format when looking at Market Data but as well under unstructured format when considering for example all the chats or voice conversations between the Bank and its clients, the News or Tweets mentioning the corporates we are dealing with, the journey of our clients on our platforms or why not satellite images of Earth!
To do this job we first need people who love working with data but are not afraid to deal with the hard problems associated to it: a big part of our job is indeed about gathering the data, understanding it, cleaning it, normalizing it and finally extracting what will be relevant to the problem in a usable format.
We then need people who are delivery driven, who know what it takes to build a robust application, who like to code but will pay attention to details as their work will go quickly in production and will be used on a daily basis by the hundreds of employees of BNPP who expect high level of reliability.
We finally need people who love to spend time modelling, being innovative in their solution, who don’t mind pressure and are not afraid to fail, and who understand that to go big, we first need to start small!
Minimum required qualifications
The required experiences are:
• 3 years + of data-science experience in industrial or academic (PhD) environment
• A master or PhD in data-science with solid bases in mathematics/statistics and computer science
• Proven track record of delivery of projects using data mining, data visualization and machine learning techniques (language such as Python/Scala/R, visualization such as Tableau…)
• Clear and deep understanding of underlying theory behind neural networks and other AI methods and ability to identify and compute relevant features
The preferred experiences are:
• Advanced degree in Machine Learning/AI
• Work experience with machine learning at a tech company
• Interest or experience with capital markets
• Fintech knowledge
• Knowledge of Databases (Oracle, SQL, MangoDB…)
• Experience with big Data environment (Hadoop, Spark)
FINRA Registrations Required: