Hybrid Catastrophe Risk Analyst, Property Analytics & ML in London
Hybrid Catastrophe Risk Analyst, Property Analytics & ML

Hybrid Catastrophe Risk Analyst, Property Analytics & ML in London

London Full-Time 36000 - 60000 £ / year (est.) No home office possible
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At a Glance

  • Tasks: Analyse natural disaster risks and develop predictive models for clients.
  • Company: International consulting firm with a top-notch Property Analytics team.
  • Benefits: Flexible working environment, professional development, and inclusive culture.
  • Why this job: Make a real impact in catastrophe risk analysis while working with advanced technologies.
  • Qualifications: Advanced qualification in Data Science and Machine Learning required.
  • Other info: Enjoy a blend of virtual work and office days in London.

The predicted salary is between 36000 - 60000 £ per year.

An international consulting firm seeks a Catastrophe Risk Analyst to join their top Property Analytics team, advising clients on natural disaster risks. The role requires an advanced qualification in Data Science and Machine Learning, and responsibilities include conducting spatial analyses, developing predictive models, and supporting client renewals.

A flexible working environment, combining virtual work and office days in London, is offered, along with an emphasis on professional development and an inclusive workplace culture.

Hybrid Catastrophe Risk Analyst, Property Analytics & ML in London employer: Aon Corporation

Join an international consulting firm that prioritises a flexible working environment and fosters a culture of inclusivity and professional growth. As a Hybrid Catastrophe Risk Analyst in London, you will have the opportunity to work with cutting-edge data science and machine learning techniques while contributing to meaningful projects that help clients navigate natural disaster risks. With a strong emphasis on employee development, this role offers a rewarding career path in a dynamic and supportive setting.
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Contact Detail:

Aon Corporation Recruiting Team

StudySmarter Expert Advice 🤫

We think this is how you could land Hybrid Catastrophe Risk Analyst, Property Analytics & ML in London

✨Tip Number 1

Network like a pro! Reach out to professionals in the catastrophe risk and property analytics fields on LinkedIn. A friendly message can go a long way, and you never know who might have the inside scoop on job openings.

✨Tip Number 2

Show off your skills! Create a portfolio showcasing your data science and machine learning projects. This is your chance to demonstrate your expertise in spatial analyses and predictive modelling, making you stand out to potential employers.

✨Tip Number 3

Prepare for interviews by brushing up on common questions related to catastrophe risk analysis. We recommend practising your responses with a friend or even in front of the mirror to build confidence and clarity.

✨Tip Number 4

Don’t forget to apply through our website! We’ve got loads of opportunities that might just be the perfect fit for you. Plus, it’s a great way to stay updated on new roles as they come up.

We think you need these skills to ace Hybrid Catastrophe Risk Analyst, Property Analytics & ML in London

Data Science
Machine Learning
Spatial Analysis
Predictive Modelling
Client Advisory Skills
Risk Assessment
Analytical Skills
Communication Skills
Project Management
Professional Development
Adaptability
Team Collaboration
Problem-Solving Skills

Some tips for your application 🫡

Tailor Your CV: Make sure your CV highlights your advanced qualifications in Data Science and Machine Learning. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects or experiences!

Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about catastrophe risk analysis and how you can contribute to our Property Analytics team. Keep it engaging and personal – we love to see your personality!

Showcase Your Analytical Skills: Since the role involves conducting spatial analyses and developing predictive models, make sure to mention any specific tools or methodologies you’ve used. We’re keen to see how you approach problem-solving and data interpretation!

Apply Through Our Website: We encourage you to apply directly through our website for a smoother application process. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates from our team!

How to prepare for a job interview at Aon Corporation

✨Know Your Data Science Stuff

Make sure you brush up on your data science and machine learning concepts. Be ready to discuss specific algorithms you've used in the past, especially those related to predictive modelling and spatial analyses. This will show that you’re not just familiar with the theory but can apply it practically.

✨Understand Catastrophe Risks

Familiarise yourself with the latest trends and challenges in catastrophe risk analysis. Research recent natural disasters and how they’ve impacted property analytics. This knowledge will help you demonstrate your understanding of the role and impress the interviewers with your insights.

✨Prepare for Scenario Questions

Expect scenario-based questions where you might need to analyse a hypothetical situation involving natural disaster risks. Practice articulating your thought process clearly and logically. This will showcase your analytical skills and ability to think on your feet.

✨Show Enthusiasm for Professional Development

Since the firm emphasises professional development, be prepared to discuss how you plan to grow in this role. Mention any relevant courses or certifications you’re interested in pursuing. This shows that you’re proactive about your career and aligns with their inclusive workplace culture.

Hybrid Catastrophe Risk Analyst, Property Analytics & ML in London
Aon Corporation
Location: London
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  • Hybrid Catastrophe Risk Analyst, Property Analytics & ML in London

    London
    Full-Time
    36000 - 60000 £ / year (est.)
  • A

    Aon Corporation

    50,000+
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