At a Glance
- Tasks: Support the team in developing tools to analyse and manage catastrophe exposure.
- Company: Leading London Market Insurance firm with a focus on innovation.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Exciting opportunity to work with cutting-edge technology in a collaborative environment.
- Why this job: Join a dynamic team and make an impact in risk assessment and analytics.
- Qualifications: Strong Python skills and experience with exposure data preferred.
The predicted salary is between 50000 - 70000 £ per year.
A leading London Market Insurance firm is looking to hire an Exposure Data Analyst to join their team. You will support the team in developing and maintaining a suite of innovative tools to track, analyse and manage natural and non-natural catastrophe accumulation exposure across the business. You will develop out process automation pipelines across the Exposure Analytics team to ensure timely updates of key analytical dashboards. You will also work with the Research team to develop proprietary risk scoring datasets for qualitative assessment of natural catastrophe hazard to deliver enhanced pricing and portfolio insight.
Key Requirements
- Strong Python capabilities
- Strong Databricks Experience
- London Market Experience is a plus
- Experience in Property insurance and/or experience dealing with exposure data is highly desirable
- Geospatial experience or experience working with geospatial datasets in Python is desirable
- Experience with Catastrophe modelling software is advantageous but not essential
Exposure Data Analyst employer: HFG
HFG is an exceptional employer that values its employees by offering a dynamic work culture and flexible hybrid working patterns, allowing for a healthy work-life balance. With a strong focus on professional development, employees are encouraged to grow their skills and advance their careers within the global insurance sector, making it a rewarding place to work for those seeking meaningful contributions in internal auditing.
