Global Market is currently recruiting talented people to join one of the most challenging and exciting part of our Quantitative Research team, the Data and Artificial Intelligence Lab!
We are currently recruiting interns in London for the Global Market Data and Artificial Intelligence Lab of BNP Paribas.
Global Market is part of the Corporate and Investment Bank and deals with all market activities on Equity, Foreign Exchange and Local Markets, G10 Rates, Primary and Secondary Credit and Financing asset classes.
The Lab mission is to leverage the latest techniques of Machine Learning (Deep Learning, Natural Language Processing) on the vast amount of structured and unstructured data we are collecting while doing our business as well as any other public source of information.
We are, among other things, building models to improve the service we give to our Clients (issuing recommendation, anticipating their needs, bringing the relevant research…), to help traders better understanding and managing their risks or leverage alternative data sources (social media, news, images…) for the benefit of our strategists.
We propose two internships:
The goal is to detect specific client/corporate situation from unstructured data in various text documents (Earning call transcript, annual reports, news,..).
The internship will be split in three different steps:
- Calibration of new state of the art language models on very specific financial corpus (like AWD, Transformer, BERT…)
- Apply those model on client situation classification tasks using either standard machine learning or more likely deep learning models
- Data augmentation may be required as our tagged dataset is limited, interacting with the relevant Sales
We are looking for an intern to develop machine-learning models for forecasting stock returns over the global trading universe (US, Europe, Asia).
Today there is a lot of data available for single stocks and the goal is to use cutting-edge forecasting models (including boosted trees and neural networks) that exploit such breadth of information.
The stock return forecasts will be plugged in the existing backtesting framework alongside the risk and transaction cost forecasts and evaluated on multiple performance metrics.
- Education in Data Science
- Not only have experience in solving complex problems but as well understand how and why the model work the way they do
- Motivated with dealing with large amount of very diverse data and extracting valuable insights out of it
- Ability to adapt quickly to new challenges, not to be afraid to experiment many times and fail before finding the right solution
- Should have a good knowledge of Python and solid background in Machine Learning
- Should have a mix of machine-learning, econometrics, programming and computer science experience