Jobbet "Investment Risk Modelling Research Project" er udløbet.
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Investment risk modelling at a glance
Investment risk modelling is essentially about generating joint paths for a large panel of risk factors, e.g., interest rates, credit spreads, implied volatilities, FX rates, and equity returns. The dimensionality of the problem poses a challenge in itself, but it is particularly challenging to accurately capture both the time series and cross-sectional properties of the risk factors (some of which are observed with different frequencies).

Research project topic
Increased computational power and large-scale machine learning methods might offer elegant solutions to the above risk modelling challenges, and this is the main topic of the research collaboration. We offer suggestions for concrete problem formulations depending on the number of ECTS credits that you have available, which can be anything from 10 to 60 ECTS.

What’s in it for you?
You will get to apply your theoretical knowledge to practical investment risk modelling problems in collaboration with experienced investment professionals and thus be faced with the nuances that are inherent to real-world risk modelling. You will have access to data that is properly prepared for analysis and therefore be able to maximize the time you spend on the research and development part of the project. Finally, we will make computational resources available to you if necessary.

The company
Fortitudo Technologies is a fintech company offering novel software solutions as well as quantitative and digitalization consultancy to the investment management industry. Learn more about the company by visiting our website and freely explore some of our technologies as open source. Requirements: 

  • You hold a BSc and are pursuing an MSc in applied mathematics, mathematics, mathematics-economics, computer science, engineering, or similar.
  • You have achieved excellent academic results and specialize in machine learning, statistical modelling, and data science.
  • Python programming experience including the most common data science and machine learning packages (SciPy Stack and TensorFlow). If you don’t satisfy this requirement, you should be willing to put in extra work to catch up before and possibly during the project.

How to apply
You can apply by doing the following:

  • Send your cover letter, CV, and university grades transcript (in Danish or English) in PDF format no later than the 1st of December.

  • Interviews will be conducted on a rolling basis, so please apply at your earliest convenience.

  • If you have any questions, please contact Anton Vorobets by email or phone (+45) 31 50 92 09.

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