Senior Quantitative Analyst

il y a 1 mois

Luxembourg, Luxembourg Banque Internationale du Luxembourg Temps plein

Banque Internationale à Luxembourg (BIL) is one of the biggest banks in the Grand Duchy, offering retail, private, corporate and institutional banking, as well as treasury and financial market services.


The majority of BIL's 2,000 employees work at the headquarters in Luxembourg City – this centralized setup promotes swift, agile decision-making and short reporting lines, enabling BIL to offer clients innovative solutions for their changing needs.

BIL is recognized as systemically important by the European Central Bank.

BIL has dedicated wealth management offices in Switzerland as well as trading floors in Luxembourg and Zurich.

In addition, BIL Group subsidiaries in Luxembourg offer specialized services:
Belair House (family office and investment management), BIL Manage Invest (alternative fund management) and BIL Lease (leasing solutions).

The Luxembourgish Government holds a 10% stake in BIL, thereby further strengthening the strategic position of the bank.

The remaining 90% of BIL are owned by the Chinese investment group Legend Holdings who are listed on the Hong Kong Stock Exchange and founded the Lenovo Group.

Founded in 1856, Banque Internationale à Luxembourg is the oldest multi-business bank in the Grand Duchy. From its foundation, the BIL has always played an active role in the development of the Luxembourg economy. It currently operates in retail, private and corporate banking, as well as on major capital markets. Employing more than 2 000 people, BIL is present in the financial hotspots that are Luxembourg, Switzerland, and China.

As a major player in Luxembourg's finance industry and as a signatory of the UN Principles of Responsible Banking, BIL is committed to handing over a responsible and sustainable bank to future generations.


Your next challenge:

  • Perform validation on all different types of models used at BIL.
  • Contribute to continuous improvement of validation methodologies and practices.
  • Contribute to the improvement of different aspects Model Risk Management (e.g. model risk measurement).
  • Validation of models of various types used in risk management and for other business purposes.
  • Writing of validation reports.
  • Presentation of validation results in the Model Risk Committee.
  • Follow-up and tracking of validation recommendations.
  • Participation in team knowledge sharing sessions.
  • Maintenance of team documentation and code library.
  • Contribution to the Model Risk Rating and other tools.

Your skills:

  • Domain of expertise: Statistics, Econometry, Mathematics or related fields
  • Education: MSc
  • Languages: English (mandatory), French (strong advantage)
  • Banking techniques: Credit Risk or Market Risk, Basel III(+),
  • IT techniques: Python, Excel, other programming experience is an advantage
* 5 to 6 years of experience in risk modelling


BIL offers a broad range of challenging projects and a huge choice of career paths .We will assist you in finding the one that best meets your skills and expectations.

Your personal development is our priority and we greatly encourage you to dive into different business areas for the broadest possible experience.

BIL is firmly of the opinion that diversity & inclusion contribute towards increasing the collective performance of the Bank. We are committed to creating a culture of inclusion that encourages individual development with equal opportunities for all.

NB:

The selected candidate will be asked to provide an extract from the criminal record (no.3) as evidence of integrity and justified with regard to the specific needs of the position to be filled.


Your skills:

  • Domain of expertise: Statistics, Econometry, Mathematics or related fields
  • Education: MSc
  • Languages: English (mandatory), French (strong advantage)
  • Banking techniques: Credit Risk or Market Risk, Basel III(+),
  • IT techniques: Python, Excel, other programming experience is an advantage
* 5 to 6 years of experience in risk modelling

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