Join Starr, a global leader in commercial insurance with over a century of expertise. We empower our employees to innovate, make impactful decisions, and build lasting client relationships worldwide. At Starr, you'll work in an entrepreneurial culture alongside accessible leaders, leveraging our financial strength and vast industry experience to deliver solutions for our clients, no matter how complex. Grow your career with a rapidly growing company that invests in its people and their ability to drive real progress.
The Group Head of Catastrophe Risk Analytics is responsible for leading Catastrophe risk analytics activities across all of Starr Group. This critical leadership position acts as a thought leader and strategic partner, ensuring world-class approaches to catastrophe risk modeling, Risk Analytics, and helps build portfolio management framework that is aligned with underwriting and enterprise risk management strategies. The ideal candidate has deep expertise in catastrophe modeling, a strong analytical mindset, exceptional communication skills, and proven leadership experience.
Key Responsibilities:
- Develop and execute the group-wide catastrophe risk analytics vision and strategy, ensuring alignment with underwriting strategy, corporate risk appetite and regulatory requirements.
- Collaborate with underwriting, claims, actuarial, finance, and IT functions to deliver robust catastrophe risk insights that inform technical pricing, portfolio optimization, accumulation control, and capital management.
- Lead group wide efforts to aggregate catastrophe risk, and deliver associated analytics that support development and execution of underwriting strategy
- Lead, inspire, and mentor a team of analytics professionals, promoting collaboration, technical excellence, and innovation.
- Evaluate emerging risks, trends, and technologies in catastrophe modeling and analytics, recommending adoption where appropriate.
- Oversee the assessment of emerging risks (e.g., climate change, new perils) and evaluate/implement state-of-the-art analytics technology and innovation.
- Partner with external vendors, data providers, and internal technology partners to develop and deploy optimized Catastrophe modeling workflows and toolset for production of risk analytics.
Qualifications:
- Advanced degree in a relevant field (e.g., Actuarial Science, Statistics, Geophysics, Engineering, Applied Mathematics, or similar).
- A minimum of 15 years’ experience in catastrophe risk analytics, with significant exposure to both insurance and reinsurance business models.
- Demonstrated experience in embedding advanced analytics and automation in catastrophe risk management processes.
- In-depth understanding of capital modeling, risk appetites, reinsurance structures, and portfolio optimization.
- Strong leadership and people management track record, including in diverse, global, matrixed organizations.
- Deep expertise in utilization of vendor catastrophe models (RMS, AIR) for development of risk analytics.
- Excellent interpersonal and communication skills; able to translate complex analytics into actionable business insights.
- Hands-on experience with advanced analytics, statistical modeling, data governance, big data platforms and visualization tools.
- Up-to-date knowledge of regulatory environments affecting catastrophe risk.
- Strong programming and data science skills (e.g., Python, R, SQL).
Starr is an equal opportunity employer, which means we'll consider all suitably qualified applicants regardless of gender identity or expression, ethnic origin, nationality, religion or beliefs, age, sexual orientation, disability status or any other protected characteristic. We recruit and develop our people based on merit and we're committed to creating an inclusive environment for all employees. We offer first class training and development opportunities to all employees. Our aim is to grow our own talent and bring out the best in people.
Starr Insurance New York, New York, USA Office
399 Park Avenue, 2nd Floor, New York, New York, United States, 10022
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