At a Glance
- Tasks: Analyse property data to drive strategic business decisions and uncover insights.
- Company: Dynamic company focused on property data analysis with a remote work culture.
- Benefits: Work from home, competitive salary, and opportunities for professional growth.
- Other info: Join a collaborative team and thrive in a fast-paced, evolving environment.
- Why this job: Make a real impact by turning data into actionable strategies in the property sector.
- Qualifications: Strong Excel and SQL skills, experience in property data analysis, and dashboard design.
The predicted salary is between 35000 - 45000 £ per year.
Please note you must have previous property data exposure to be considered either within PBSA/BTR or multifamily property).
Are you passionate about uncovering insights from data and turning them into strategies that drive business success?
We're looking for a Data Analyst who can blend technical expertise with commercial awareness.
In this role, you'll have the chance to shape pricing, marketing, sales, and operational decisions by putting evidence at the heart of our strategy.
You'll work across teams, combining competitor intelligence, internal reporting, and advanced analysis to help guide key decisions.
From monitoring rental trends to supporting financial modelling, this role is all about making sure the numbers tell the right story.
What You'll Be Doing Your day-to-day will be varied and hands-on.
Typical tasks include
- Tracking occupancy levels, rental values, and competitor benchmarks.
- Building demand models that account for seasonality and market changes.
- Producing forecasts to support revenue growth and leasing plans.
- Developing dashboards and visual reports in tools such as Power BI or Tableau.
- Collaborating with colleagues on pricing strategies and proposals.
- Ensuring smooth data migration when new systems are introduced.
- Validating and cleaning data to maintain integrity and reliability.
- Documenting processes and testing outputs for accuracy.
- Delivering ad-hoc reports for strategic projects or expansion initiatives.
What You'll Need to Succeed We're looking for someone who enjoys working with complex data but can also explain insights in a simple way. You'll need:
- Strong skills in Excel and SQL.
- Experience designing dashboards and data visualisations.
- Attention to detail, with excellent problem-solving ability.
- The confidence to communicate findings to people with or without technical knowledge.
- The ability to work to deadlines while juggling several projects
- Experience in property.
- Familiarity with revenue management or dynamic pricing.
- Exposure to CRM or property management systems (e. g., Star Rez, Entrata, Salesforce, Hubspot).
Who You Are We'd love to meet someone who is
- A self-starter who thrives on solving problems.
- Curious about data and eager to apply it in practical, commercial ways.
- Organised, reliable, and comfortable with shifting priorities.
- Proud of delivering insights that make a real difference.
Job Type: Full-time Benefits
- Work from home
StudySmarter Expert Advice🤫
We think this is how you could land Remote Data Analyst Property in Paisley
✨Get Involved in Data Science Meetups
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Grabjobs.
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We think you need these skills to ace Remote Data Analyst Property in Paisley
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Grabjobs, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Grabjobs. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Grabjobs
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Grabjobs!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
