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- Tasks: Lead a team in building and developing analytics models.
- Company: Join a growing underwriting solutions business making waves in the insurance industry.
- Benefits: Competitive salary up to £110,000 and flexible work with 2 days in the London office.
- Why this job: Be at the forefront of data science in a dynamic environment with real impact.
- Qualifications: Strong Python experience and background in insurance or pricing functions required.
- Other info: Reach out for more details at rafaela.fakhre@eamesconsulting.com or call 07456961050.
The predicted salary is between 66000 - 88000 £ per year.
A multinational company is looking to hire a Data Science Team Lead to join their London office. They have a hybrid set up of 2 days a week in the office. They are looking to hire someone to support the development of various analytics models whilst also supporting and developing junior members of staff. This is very varied role and would suit someone who enjoys being creative with modern data science techniques. You will be building out new analytics products, producing models using GLMs and GBMs, and executing data analysis. For this role the team are keen to speak to those in general insurance pricing who have a Masters or PhD. Individuals should have solid programming ideally with Python and SQL, and have experience with data manipulation. For more information please do reach out via email at hannah.turner@eamesconsulting.com Eames Consulting is acting as an Employment Agency in relation to this vacancy.
Data Science Lead employer: Eames Consulting
Contact Detail:
Eames Consulting Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Science Lead
✨Tip Number 1
Make sure to highlight your experience in building and developing analytics models. Be ready to discuss specific projects where you've successfully implemented these models, especially in the insurance or pricing domain.
✨Tip Number 2
Demonstrate your leadership skills by preparing examples of how you've managed teams in the past. Think about challenges you faced and how you motivated your team to achieve their goals.
✨Tip Number 3
Brush up on your Python skills and be prepared to discuss your proficiency. You might want to showcase any relevant projects or contributions to open-source that demonstrate your coding capabilities.
✨Tip Number 4
Since this role requires being in the office two days a week, consider how you can express your flexibility and willingness to collaborate with the team in person. This can set you apart from other candidates.
We think you need these skills to ace Data Science Lead
Some tips for your application 🫡
Understand the Role: Make sure to thoroughly read the job description for the Data Science Lead position. Highlight the key responsibilities and required skills, such as building analytics models and managing a team.
Tailor Your CV: Customize your CV to emphasize your experience in data science, particularly in insurance and pricing functions. Showcase your strong Python skills and any relevant projects or achievements.
Craft a Compelling Cover Letter: Write a cover letter that reflects your passion for data science and leadership. Mention specific examples of how you've successfully built analytics models and led teams in the past.
Highlight Relevant Experience: In your application, make sure to highlight any previous roles that align with the requirements of the position. Discuss your experience in the insurance sector and any relevant projects that demonstrate your expertise.
How to prepare for a job interview at Eames Consulting
✨Showcase Your Python Skills
Since strong Python experience is a must for this role, be prepared to discuss your proficiency in Python. Bring examples of analytics models you've built and be ready to explain your thought process and the impact of your work.
✨Demonstrate Leadership Experience
As a Data Science Lead, you'll be managing a team. Highlight your previous leadership roles and how you've successfully guided teams in developing analytics solutions. Share specific examples of challenges you faced and how you overcame them.
✨Understand the Insurance Industry
Familiarize yourself with the insurance sector, particularly in pricing functions. Be ready to discuss how data science can enhance underwriting processes and improve decision-making in this field.
✨Prepare Questions for the Interviewers
Engage with your interviewers by preparing insightful questions about the company's goals, team dynamics, and the specific challenges they face in data science. This shows your genuine interest in the role and helps you assess if it's the right fit for you.
