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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 market-leading Modelling and Intelligence team.
- Benefits: Fixed-term contract with opportunities for professional growth and collaboration.
- Other info: Dynamic role with collaboration across Data Science, Engineering, and Innovation teams.
- 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 are essential.
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.
Statistical Data Analyst in London employer: Norton Blake
Contact Detail:
Norton Blake Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Statistical Data Analyst in London
✨Tip Number 1
Network like a pro! Reach out to professionals in the property and data science sectors on LinkedIn. Join relevant groups and engage in discussions to get your name out there and show off your enthusiasm for econometrics and data analysis.
✨Tip Number 2
Prepare for interviews by brushing up on your Python skills and familiarising yourself with real estate modelling concepts. We recommend practising common interview questions related to data analysis and being ready to discuss your past projects in detail.
✨Tip Number 3
Showcase your analytical prowess! Create a portfolio of your work that highlights your experience with econometric models and data visualisation. This will give you an edge and demonstrate your ability to deliver insights effectively.
✨Tip Number 4
Don’t forget to 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 and genuinely interested in joining our team.
We think you need these skills to ace Statistical Data Analyst in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Statistical Data Analyst role. Highlight your experience with econometric and statistical techniques, and don’t forget to mention your Python skills. We want to see how you can bring value to our Modelling and Intelligence team!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're excited about this role and how your background aligns with our needs. Be sure to mention any relevant projects you've worked on that showcase your analytical skills.
Showcase Your Projects: If you've worked on any interesting data science or econometric projects, make sure to include them in your application. We love seeing real-world applications of your skills, especially those that relate to real estate modelling and forecasting.
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you’re considered for the role. Plus, it’s super easy to do!
How to prepare for a job interview at Norton Blake
✨Know Your Data Science Stuff
Make sure you brush up on your econometric and statistical techniques before the interview. Be ready to discuss how you've applied these methods in real-world scenarios, especially in relation to property and market analysis.
✨Show Off 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 a project where you used Python to analyse data. Practice coding challenges related to data manipulation and analysis.
✨Collaborate Like a Pro
This role involves working with various teams, so be prepared to talk about your experience collaborating with cross-functional groups. Share examples of how you’ve worked with data engineers or other analysts to deliver insights and how you handle differing opinions.
✨Attention to Detail is Key
Highlight your meticulous nature when it comes to data analysis and reporting. Bring examples of past projects where your attention to detail made a significant impact, whether it was catching an error or improving the accuracy of a model.