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- Tasks: Join a dynamic team to develop cutting-edge climate modelling software and enhance insurance analytics.
- Company: BirdsEyeView creates innovative solutions for the reinsurance industry, focusing on natural catastrophe modelling.
- Benefits: Enjoy competitive salary, health insurance, pension scheme, 26 days holiday, and remote work options.
- Why this job: Make a real impact in climate data science while growing your skills in a collaborative environment.
- Qualifications: Bachelor's in a quantitative field; strong Python skills and knowledge of data science techniques required.
- Other info: This is an on-site role, requiring presence in the office at least 4 days a week.
The predicted salary is between 36000 - 60000 £ per year.
BirdsEyeView builds Natural Catastrophe Modelling software for reinsurance and insurers globally. The software enables specialty insurers to model future floods, wildfires, tropical cyclones, and more.
The BirdsEyeView technology ecosystem includes a massive climate data infrastructure, scalable product development, and machine learning data cleansing and ingestion. By combining all these factors, BirdsEyeView helps underwriters assess their exposures to growing climate-change-induced natural catastrophes.
WEATHER ANALYTIXâ„¢ provides our insurance partners with cutting edge climate and weather analytics models, helping them assess their exposure to natural catastrophes and improve their underwriting performance.
You’ll be joining a small team of scientists, software developers and insurance experts and will have a unique opportunity to impact many levels of the firm, such as business applications and product development. This is an ideal position for someone interested in working with large climate and geospatial data, building new technologies, while taking a deep dive into the insurance industry. The role will present great opportunities for growth and you will play an important role in other areas of the business.
- Support the strategy and its implementation for the WEATHER ANALYTIXâ„¢ platform - on time and to budget.
- Work closely with the Business Development teams to define and help deliver new products and enhancements.
Qualifications:
- Bachelor's degree in Computer Science, Statistics, Physics or a related quantitative field. Master’s degree preferred.
- Strong proficiency in Python for data manipulation and machine learning.
- Knowledge of statistical and data science modelling techniques.
- Understanding of web systems architecture, design, and development.
- Ability to work autonomously with excellent time, budget, and project management skills.
- Experience working with JIRA, Confluence, GitLab, AWS.
- Experience working with ERA5/Climate Data.
Compensation:
- Competitive salary and performance-based incentives.
- Health Insurance & Pension Scheme.
- 26 days per year holiday.
- 7 Days Work from Abroad Policy.
- Office Location: City of London.
Important: This is an on-site role with applicants expected in office minimum 4 days/week.
Data Scientist - Machine Learning, AI, Python employer: BirdsEyeView
Contact Detail:
BirdsEyeView Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist - Machine Learning, AI, Python
✨Tip Number 1
Familiarise yourself with the specific climate data tools and technologies mentioned in the job description, such as ERA5 and AWS. Having hands-on experience or projects that demonstrate your ability to work with these tools can set you apart from other candidates.
✨Tip Number 2
Network with professionals in the insurance and geospatial data fields. Attend relevant meetups or webinars to connect with people who work at BirdsEyeView or similar companies. This can provide you with insider knowledge and potentially a referral.
✨Tip Number 3
Showcase your Python skills through personal projects or contributions to open-source projects related to machine learning or data science. Highlighting your practical experience in your discussions can demonstrate your capability and enthusiasm for the role.
✨Tip Number 4
Prepare to discuss how you would approach the challenges of modelling natural catastrophes. Think about specific examples or case studies where you've applied statistical and data science techniques to solve complex problems, as this will resonate well with the interviewers.
We think you need these skills to ace Data Scientist - Machine Learning, AI, Python
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in data science, machine learning, and Python. Include specific projects or roles where you've worked with geospatial data or climate analytics to demonstrate your fit for the role.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the position and the company. Mention how your background aligns with BirdsEyeView's mission and how you can contribute to their WEATHER ANALYTIXâ„¢ platform.
Showcase Technical Skills: Clearly outline your proficiency in Python and any experience with statistical modelling techniques. If you have worked with tools like JIRA, Confluence, or AWS, be sure to mention these as they are relevant to the role.
Highlight Team Collaboration: Since the role involves working closely with business development teams, emphasise any past experiences where you collaborated with cross-functional teams. This will show your ability to work autonomously while also being a team player.
How to prepare for a job interview at BirdsEyeView
✨Showcase Your Technical Skills
Make sure to highlight your proficiency in Python, especially in data manipulation and machine learning. Be prepared to discuss specific projects where you've applied these skills, as well as any experience with tools like JIRA, Confluence, and GitLab.
✨Understand the Industry
Familiarise yourself with the insurance sector and how climate change impacts it. Being able to discuss how BirdsEyeView's technology can help insurers model natural catastrophes will demonstrate your interest and understanding of the role.
✨Prepare for Problem-Solving Questions
Expect questions that assess your analytical thinking and problem-solving abilities. Practice explaining your thought process when tackling complex data challenges, particularly those related to geospatial data or climate analytics.
✨Demonstrate Team Collaboration
Since you'll be working closely with a small team, emphasise your ability to collaborate effectively. Share examples of past experiences where you successfully worked with others to achieve a common goal, especially in a technical environment.
