Long Term Internship - Global Markets - Quantitative Research - Hong Kong
Position Purpose
BNP Paribas has a presence in 74 countries with over 190,000 employees. It ranks highly in its two core activities: Retail Banking and Services as well as Corporate & Institutional Banking.
In Asia Pacific, the BNP Paribas Group is a leading employer with more than 15,000 employees* and a presence in 14 markets. Being one of the largest international banking networks, we strive to employ talented and innovative people who are aligned to our vision and culture.
* excluding partnerships
Main Duties and Responsibilities
The Global Market Quantitative Research team provides cutting-edge pricing and risk management solutions to traders, marketers and risk managers based on innovative mathematical, statistical and technological research. With around 200 people globally, the team is present in Europe, Asia (Singapore, Hong Kong and Tokyo) and the Americas.
As a trainee you will be exposed to a leading financial engineering platform developed for financing and collateral transformation (FCT). One of the main objectives of FCT is to ensure an optimal use of the bank’s securities inventory with regards to client profitability, funding and capital costs; alongside stock lending & borrowing. This is an exciting and challenging opportunity to learn about one of the major businesses of the bank and to contribute to an innovative pricing platform, using optimization and machine learning, and to help drive the business decisions.
- Marking of securities: data analysis, ML, statistical modeling
- Security financing market modeling
- Pricing and optimization of security financing transactions
- Data analysis to estimate a client profitability and trade impacts
Technical and Behavioural Competencies
- Methodical and innovative thinker
- Problem solver with a keen eye for detail
- Strong drive to exceed expectations and take initiative
- Confident verbal communication skills
- Strong knowledge of optimization, linear algebra, dynamic programing and numerical methods
- Solid statistical modeling and data analysis skills
- Hands-on programming skills (Python, C++, C# preferred)
This opportunity is closed to applications.