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- Tasks: Apply data science and machine learning to tackle commercial insurance challenges.
- Company: Leading insurance firm in London with a focus on innovation.
- Benefits: Hybrid working model, extensive holiday, and a pension plan.
- Why this job: Make a real impact while mentoring others in a dynamic environment.
- Qualifications: Experience in commercial insurance and strong Python skills.
- Other info: Opportunity for career growth and leading analytical projects.
The predicted salary is between 43200 - 72000 £ per year.
A leading insurance firm in London is seeking a Senior Data Scientist to apply data science and machine learning to commercial insurance challenges.
Responsibilities include:
- Delivering impactful analytics
- Leading analytical projects
- Mentoring junior Data Scientists
The role offers a hybrid working model and a range of benefits, including extensive holiday and a pension plan.
Candidates should have experience in commercial insurance and strong Python skills.
Senior Data Scientist — Pricing & Risk (Hybrid London) employer: QBE Insurance Group
Contact Detail:
QBE Insurance Group Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Scientist — Pricing & Risk (Hybrid London)
✨Tip Number 1
Network like a pro! Reach out to people in the insurance and data science fields on LinkedIn. A friendly chat can open doors and give you insights into the company culture.
✨Tip Number 2
Show off your skills! Prepare a portfolio showcasing your data science projects, especially those related to pricing and risk. This will help you stand out during interviews.
✨Tip Number 3
Practice makes perfect! Brush up on your Python skills and be ready to tackle technical questions or coding challenges. We all know that confidence is key!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Senior Data Scientist — Pricing & Risk (Hybrid London)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in commercial insurance and showcases your strong Python skills. We want to see how your background aligns with the role, so don’t be shy about emphasising relevant projects!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about data science in the insurance sector and how you can deliver impactful analytics. Let us know what makes you the perfect fit for our team.
Showcase Your Projects: If you've led analytical projects or mentored junior Data Scientists, make sure to mention these experiences. We love seeing examples of your work and how you’ve made a difference in previous roles!
Apply Through Our Website: We encourage you to apply directly through our website. 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 QBE Insurance Group
✨Know Your Data Science Stuff
Make sure you brush up on your data science and machine learning concepts, especially as they relate to commercial insurance. Be ready to discuss specific projects you've worked on and how you've applied Python in real-world scenarios.
✨Showcase Your Analytical Skills
Prepare to talk about how you've delivered impactful analytics in previous roles. Think of examples where your insights led to significant business decisions or improvements, and be ready to explain your thought process.
✨Mentorship Matters
Since this role involves mentoring junior Data Scientists, be prepared to discuss your experience in guiding others. Share examples of how you've helped colleagues grow their skills and how you approach teaching complex concepts.
✨Get Familiar with the Company
Research the insurance firm and understand their challenges in pricing and risk. This will help you tailor your answers and show that you're genuinely interested in how you can contribute to their success.
