At a Glance
- Tasks: Analyse performance of 40,000 rent accounts and provide data-driven insights.
- Company: Join Stonewater, a national leader in income management.
- Benefits: Hybrid working, competitive salary, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on maximising income and performance.
- Why this job: Make a real difference in customer outcomes through data analysis.
- Qualifications: Strong analytical skills and experience with data interpretation.
The predicted salary is between 35000 - 42000 £ per year.
Stonewater is seeking an analytical and insight-driven Income Performance Analyst to join our national Income team, shaping how we maximise income, improve performance and deliver excellent customer outcomes.
You will analyse performance across approximately 40,000 rent accounts, providing forecasting, dashboards and data-led recommendations to reduce arrears and support tenancy sustainment, with collaboration across Income, Finance and Data teams.
Data-Driven Income Performance Analyst (Hybrid) in Oxford employer: TipTopJob
At TipTopJob, we pride ourselves on being an exceptional employer, offering a supportive work culture that values collaboration and professional growth. Located in the serene surroundings of Thatcham, Berkshire, our mental health hospital provides extensive benefits, including a competitive salary and opportunities for continuous development, making it an ideal place for dedicated professionals to thrive in their careers while making a meaningful impact on patients' lives.
StudySmarter Expert Advice🤫
We think this is how you could land Data-Driven Income Performance Analyst (Hybrid) in Oxford
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We think you need these skills to ace Data-Driven Income Performance Analyst (Hybrid) in Oxford
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 TipTopJob, 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 TipTopJob. 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 TipTopJob
✨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!
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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
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✨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.
