Overview

About us

Starr is a global insurance and investment organisation providing property and casualty insurance solutions to business and industry. Starr has grown into one of the world's fastest-expanding insurance organisations, writing business in 128 countries across six continents. Our talented and experienced associates manage risk and ultimately support the profitable growth of organisations in a dynamic, competitive and ever-changing marketplace. Join us and become part of our talented and passionate workforce.

Role Purpose

The role supports the Exposure Analytics Manager in developing and maintaining a suite of innovative tools to track, analyse and manage natural and non natural catastrophe accumulation exposure across the business.

This role will suit a candidate with a strong background in coding and with interest in problem solving and process automation. Experience working with exposure datasets is desirable but not essential.

It is a unique opportunity to work in a rapidly evolving and fast-growing environment, where exposure risk management and analytics are at the core of our business.

Key Responsibilities

• Support the Exposure Analytics Manager in providing forward-looking views of the active natural catastrophe portfolio as part of the annual syndicate business forecast (SBF) exercise and explore methods to automate delivery of key metrics.
• Develop out process automation pipelines across the Exposure Analytics team to ensure timely updates of key analytical dashboards (e.g. using Databricks, Power BI).
• Work with the Research team to develop proprietary risk scoring datasets (e.g. using open-source geospatial data) for qualitative assessment of natural catastrophe hazard to deliver enhanced pricing and portfolio insight.
• Support the development of code pipelines to allow underwriters and senior stakeholders to view their natural catastrophe portfolio of exposures on different views of risk (e.g. climate conditioned views) consistent with the latest scientific research.
• Develop code to translate natural catastrophe simulated loss outputs into various forms required throughout the business (Capital, Active Portfolio Management).
• Work with the core Exposure Management team to help develop novel tools to enhance accuracy and consistency of natural catastrophe pricing outputs and reduce manual workloads through Python/SQL solutions.

The above duties and responsibilities are not an exhaustive list, and you may be required to undertake other reasonable duties compatible with your experience and competencies. This description may be varied from time to time to reflect changing business requirements.

Qualifications, skills and experience

• Proven experience working in the Insurance sector in a data-focussed role.
• Experience in Property insurance and/or experience dealing with exposure data is highly desirable.
• Experience with Catastrophe modelling software (RMS/Verisk) is advantageous but not essential.
• Programming skills, preferably Python and SQL.
• Familiarity with front-end dashboarding solutions (e.g. HTML/React/Dash) is advantageous.
• Geospatial experience (e.g. ArcGIS/QGIS) or experience working with geospatial datasets in Python is desirable.
• Use of AI tools (e.g. MS Copilot) and AI code assistance.
• Experience using API's is desirable.

Core behavioural competencies

• Analysis and decision making
• Innovation and problem solving
• Performance focus
• Resilience and adaptability

Additional Information

• A full job description can be seen here

Starr is an equal opportunity employer, which means we will 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.

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