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- Tasks: Deliver innovative data-driven insights and analysis in real estate modelling and forecasting.
- Company: Join a leading property and research environment with a focus on collaboration.
- Benefits: Fixed-term contract with opportunities for professional growth and development.
- Other info: Work in a dynamic central London office with a supportive team culture.
- Why this job: Make an impact by solving real-world business problems with data science techniques.
- Qualifications: Experience in econometrics, statistics, and strong Python skills required.
The predicted salary is between 40000 - 50000 £ per year.
Be part of a team delivering innovative data-driven insight and analysis within a leading property and research environment. An exciting opportunity has arisen for an enthusiastic and innovative Econometrician/Data Scientist to join a market-leading Modelling and Intelligence team.
The successful candidate will work on research projects focused on real estate modelling, forecasting, and analytical insight generation. The role will involve enhancing existing econometric and statistical models, while also developing new analytical projects in collaboration with stakeholders across multiple real estate sectors.
This is a research-oriented position ideally suited to candidates with experience applying econometric, statistical, and data science techniques to solve commercial business problems. Based in a central London office, the role plays a key part in delivering analytical insight and strategic intelligence to internal stakeholders and clients across an international network.
Reporting to the Head of Data Science, the position is offered on a fixed-term contract basis. Candidates should be comfortable working independently while also collaborating closely with cross-functional analytics and research teams. The role sits within the Modelling and Intelligence function as part of a wider Global Research division.
Day-to-day management will be provided by the Head of Data Science.
- Collaborate with colleagues across Data Science, Data Engineering, Geospatial, and Innovation teams to deliver econometric and data science analysis relating to real estate trends and market behaviour.
- Strong Python capability is essential.
- Experience with Databricks and Azure DevOps is highly desirable, though not mandatory.
- Produce regular analytical outputs, dashboards, reports, and data books for internal stakeholders.
- High attention to detail and accuracy in data analysis and reporting.
- Self-motivated with the ability to independently manage projects and deadlines.
Data Statistician in London employer: Norton Blake
Contact Detail:
Norton Blake Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Statistician in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with professionals on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your econometric and data science projects. Use GitHub or a personal website to display your work, especially if you've tackled real estate modelling or forecasting.
✨Tip Number 3
Prepare for interviews by brushing up on your Python skills and familiarising yourself with tools like Databricks and Azure DevOps. Be ready to discuss how you've applied these in past projects to solve business problems.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Data Statistician in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that match the Data Statistician role. Highlight your econometric and statistical expertise, especially any projects where you've applied these techniques to solve real-world problems.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're excited about this opportunity. Share specific examples of your work in data science and how it relates to real estate modelling. Show us your enthusiasm for joining our innovative team!
Showcase Your Technical Skills: Since strong Python capability is essential, make sure to mention any relevant projects or experiences where you've used Python. If you have experience with Databricks or Azure DevOps, don’t forget to include that too!
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’re considered for the role. Plus, it shows us you’re keen on being part of our team!
How to prepare for a job interview at Norton Blake
✨Know Your Data Inside Out
Make sure you brush up on your econometric and statistical models before the interview. Be ready to discuss how you've applied these techniques in past projects, especially in real estate contexts. This will show your potential employer that you can hit the ground running.
✨Showcase Your Python Skills
Since strong Python capability is essential for this role, prepare to demonstrate your coding skills. You might be asked to solve a problem or explain your approach to data analysis using Python. Practise common data manipulation tasks and be ready to discuss libraries like Pandas and NumPy.
✨Prepare for Collaboration Questions
This role involves working closely with cross-functional teams, so expect questions about teamwork and collaboration. Think of examples where you've successfully worked with others, particularly in analytics or research settings, and be ready to share how you contributed to the team's success.
✨Be Ready to Discuss Tools and Technologies
While experience with Databricks and Azure DevOps is desirable but not mandatory, it’s good to familiarise yourself with these tools. If you have experience with similar platforms, be prepared to discuss how you’ve used them in your work. This shows your adaptability and willingness to learn new technologies.