Senior Natural Catastrophe Specialist, Vulnerability

About the Role

As a Senior Natural Catastrophe Specialist with expertise in Vulnerability, you will help advance how we quantify vulnerability in catastrophe modeling to better understand the physical and economic impact of natural perils like tropical cyclones, earthquakes, severe convective storms, floods, winter storms, and wildfires.

Key responsibilities include :

• Advance our vulnerability modeling of natural catastrophes and insured losses to buildings, contents, and business interruption.
• Quantify the role of important building characteristics and regional differences in construction practices on vulnerability and losses.
• Analyze claims data to empirically develop and validate vulnerability curves for loss estimation.
• Establish yourself as an expert on natural catastrophe risk and modeling within Swiss Re and effectively communicate these risk views internally and externally.
• Collaborate with other business functions like underwriting, portfolio steering and risk management to translate vulnerability insights into actionable decisions.

About the Team

The catastrophe perils (Cat Perils) department is responsible for development and advancement of Swiss Re’s global Nat Cat risk assessment framework, models, and tools. The team consists of approximately 70 dedicated scientists and engineers in multi-faceted technical fields. We develop proprietary Nat Cat models and risk views, informed by the latest science and engineering as well as our unique underwriting experience and expertise, to grow Swiss Re's edge in Nat Cat re/insurance. We create value for Swiss Re by driving confident Nat Cat decisions, in close collaboration with peers in underwriting and other functions.

About You

You are passionate about shaping the future of Swiss Re's Nat Cat modeling landscape! You are a problem-solver who excels in applying advanced scientific methods to complex challenges in natural catastrophe modeling. Highly motivated, detailed-oriented and well organized you thrive in a fast-paced business setting. You are comfortable performing self-directed work with outstanding written and verbal communication skills.

We are looking for candidates who also meet these requirements :

• Minimum of 3 years of industry or academic experience in catastrophe modeling or risk assessment.
• Exceptional fundamentals developing, calibrating, and validating vulnerability models of buildings and infrastructure systems to natural catastrophes for loss prediction. Relevant lines of business include residential, commercial, and/or industrial structures.
• M.Sc. or Ph.D. degree in civil engineering or a related technical field.
• Strong analytical and programming skills (e.g., Python, Matlab, R), and experience using statistical tools and methods to solve applied problems.

Additional nice-to-haves include :

• Catastrophe modeling experience in a (re)insurance context.
• Familiarity with emerging vulnerability topics such as climate change adaptation, uncertainty quantification, data centers, business interruption and social inflation.
• Application of AI and machine learning in damage assessment, claims analysis and vulnerability estimation.
• Proficiency with GIS software and structured databases.

Our company uses a hybrid work model requiring at least three days in the office each week, with the option to work onsite full-time if preferred. This position is not eligible for full-time telework.

The estimated base salary range for this position is $164,000 to $246,000. The specific salary offered for this or any given role will take into account a number of factors including but not limited to job location, scope of role, qualifications, complexity/specialization/scarcity of talent, experience, education, and employer budget. At Swiss Re, we take a "total compensation approach" when making compensation decisions. This means that we consider all components of compensation in their totality (such as base pay, short-and long-term incentives, and benefits offered), in setting individual compensation.