Quantitative Developer - Python

Markets Quantitative Analysis Department (MQA) is a division of the Global Markets  

business and has responsibility for providing the analytical models which are used for pricing securities and risk managing the Firms positions throughout the Markets businesses. The scope of this work extends from the research into the mathematical derivation of the model through the coding testing and documentation of the model for formal validation and approval and finally to delivering the model both to the desktop and to Technology for incorporation into the Firms books and records systems.

Tasks

• Development and maintenance of the inhouse python Analytical infrastructure
• Advancing the quantitative toolbox by developing new technologies algorithms and numerical techniques
• Work on Regulatory and Governance based projects across a range of the asset
  classes.
• Work with Quant teams to standardize regulatory and infrastructural solutions.
• The candidate will have daily contacts with supervisor(s) and will receive interactive training from various members of the Global team including introduction and intermediary financial courses as well as Business training. The candidate will also participate to the weekly and daily team meetings across regions.
• Participate on meetings inperson in the Budapest office.

Requirements

• Excellent command of programming using Python proven track record of Python
  projects
• Experience in a Quantitative Developer role or other finance/regulatory python
  development is an advantige
• Good understanding distributed infrastructures: MQs Service discovery Python
  Celery SQL Elastic Search MongoDB is an advantage.
• Understanding React or other frontend web development frameworks is an
  advantage.
• Proven track record of development and support analytics library such as Rates
  Credit Equities Commodities as advantagePrevious experience working on Regulatory based projects such as Model Risk Basel III Stress Testing CCAR PAA an advantage.
• BSc / MSc in Software Engineering Computer Science Natural Sciences or
  Economics