Bezeichnung:  Internship Tactical Asset Allocation - Data Analytics (m/f/d)*

Stelle ID:  9805
Standort: 

München, BY, DE

Einrichtung:  Munich Re
Beschreibung: 

The Group Investment Management (GIM) division is responsible for managing the proprietary Munich Re and ERGO investment portfolio of around €250bn. Within the business unit Investment Strategies of GIM the Tactical Asset Allocation department (GIM1.2) is responsible for the tactical asset allocation (TAA) of the whole Munich Re Group. This implies that on the basis of the long term strategic allocation (SAA) a granular tactical asset allocation is built and regularly updated to adjust for the changes in the political/economic environment and movements in the capital markets. The department unites experts for all liquid asset classes. Portfolio positions in all asset classes are weighted on the basis of quantitative and qualitative methods to reach an efficient portfolio (TAA generation).

Within this challenging environment you will strengthen the team as an intern within the department “Tactical Asset Allocation“. A candidate with strong quantitative and programming skills and experiences in data analytics is needed.

 

Your Job

Support the team in developing methods, models and processes for a structured analysis of capital markets, specifically

  • Design back-testing frameworks to evaluate the predictive accuracy of covariance matrices, apply methodological advances in the literature and improve our model risk forecasting capabilities.
  • Work at the intersection of data analytics and portfolio strategy, leveraging cutting-edge technologies like Azure Data Lake, Databricks, and Lakehouse architecture.
  • Support in the continuous improvement of our proprietary portfolio data analysis application
  • Automation of capital market tools as well as portfolio steering tools
  • Participating in projects on investment topics


Your Profile

  • Student of math, science, information technology, economics or business management with a quantitative orientation and excellent marks
  • Knowledge of quantitative methodologies, statistics, financial mathematics
  • Proficient programming skills is a prerequisite, preferably Python
  • Knowledge in capital markets and good macroeconomic understanding
  • Data analysis and machine learning is a plus

 

Students from countries outside the EU require a German residence-/work permit. The first master’s degree may not have been completed before/during the entire internship

 

About us

As the world's leading reinsurance company with more than 11,000 employees at over 50 locations, Munich Re introduces a paradigm shift in the way you think about insurance. By turning uncertainty into a manageable risk we enable fundamental change. Join us working on topics today that will concern society tomorrow, whether that be climate change, major construction projects, medical risk assessment or even space travel.

Together we embrace a culture where multiskilled teams dare to think big. We create the new and the different for our clients and cultivate innovation.

Sounds like you? Push boundaries with us and be part of Munich Re. Our employees are our greatest strength. That’s why we offer them a wide range of benefits.

 

Unlock your potential

  • Diversity, Equity & Inclusion: we embrace the power of differences and are convinced that diversity fosters innovation and resilience and enables us to act braver and better.
  • Continuous Learning: we believe that continuous learning is a key differentiator and critical for building new skills and accelerating growth.
  • Career Mobility: we actively support career mobility, and our strong global and regional presence offers a wealth of career growth opportunities for you.

 

Münchener Rückversicherungs-Gesellschaft

Silke Rößler / Oksana Kern • Coordination Students Program
Königinstraße 107 • 80802 München • GERMANY

 

* Munich Re not only stands for fairness with regard to its clients; it is also an equal opportunity employer. Severely disabled candidates will also be prioritised, if equally qualified.