Senior Cat Modeller for severe weather perils

vor 2 Monaten

Zürich, Zürich, Schweiz Swiss Re AG Vollzeit
Location:

Zurich, Zurich, CH

Senior Cat Modeller for severe weather perils (Hybrid / m/f/x/d / 80% - 100%)

Are you looking for a unique opportunity to bring our catastrophe model development to the next level? Do you want to be at the intersection of science and its business application for re/insurance and curious in how our insights impact Underwriting decisions? Then this could be for you

About the Role

In this role you will work closely with peers in our global Cat Perils team and with the business units who make decisions based on our models.

Key responsibilities:

  • Contribute to Cat Perils global agenda with a business impact attitude, delivering tangible outcomes with a sense of urgency.
  • Build, release, maintain and communicate on the next generation of Nat Cat models globally, with a focus on Severe Convective Storms (SCS) and, other weather-related perils.
  • Contribute to and lead cross regional team projects.
  • Establish yourself as a point of contact for Nat Cat related questions, with senior expertise in severe weather perils (e.g. Hail, Tornado).

About the Team

The Cat and Geo modelling team is part of Swiss Re's global in-house natural catastrophe modelling unit 'Cat Perils'. We create value for Swiss Re by driving confident Nat Cat decisions.
With our proprietary modelling technology for natural catastrophe perils, we bring latest science to underwriting decision-making, working closely with peers in re/insurance underwriting and further support our business in all matters related to natural catastrophe risk.

About You

You are a dedicated teammate who can approach problems with flexibility and enjoys taking responsibility. With your excellent interpersonal skills, you can build trust with peers in the business teams by providing relevant and deep insights. You balance technical excellence with business needs in a pragmatic way and are agile in responding to stakeholders' needs, always focusing on where it matters most.

Technical skills and education:

  • M.Sc. or Ph.D. degree in quantitative natural sciences, engineering or equivalent
  • 4+ years of industry or post PhD academic experience in natural catastrophe modelling (probabilistic models ideally for SCS), preferably in a re/insurance context. Underwriting experience is a plus.
  • In-depth knowledge of climate-change-related topics and extreme weather events in particular.
  • Professional network within the academic and/or industry natural catastrophe modelling community. OASIS LMF experience is a plus.
  • Strong analytical and programming skills (e.g., Python, Matlab, Git), record in successfully developing data analysis applications. Experience with statistics and machine learning is a plus.
  • Strong project management, communication, and presentation skills
  • Excellent written and verbal English; additional languages are a plus

We are looking forward to your application

About Swiss Re

Swiss Re is one of the world's leading providers of reinsurance, insurance and other forms of insurance-based risk transfer, working to make the world more resilient. We anticipate and manage a wide variety of risks, from natural catastrophes and climate change to cybercrime. Combining experience with creative thinking and cutting-edge expertise, we create new opportunities and solutions for our clients. This is possible thanks to the collaboration of more than 14,000 employees across the world.

Our success depends on our ability to build an inclusive culture encouraging fresh perspectives and innovative thinking. We embrace a workplace where everyone has equal opportunities to thrive and develop professionally regardless of their age, gender, race, ethnicity, gender identity and/or expression, sexual orientation, physical or mental ability, skillset, thought or other characteristics. In our inclusive and flexible environment everyone can bring their authentic selves to work and their passion for sustainability.

Keywords:
Reference Code: 129477

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