At a Glance
- Tasks: Support buying and merchandising teams by raising purchase orders and tracking data.
- Company: Dynamic, design-led company in Greater London with a focus on innovation.
- Benefits: Hybrid work model, opportunities for growth, and a creative work environment.
- Other info: Perfect for those eager to learn and grow in the merchandising field.
- Why this job: Join a team where your analytical skills can shine and make a real impact.
- Qualifications: Strong analytical skills and familiarity with business systems like Acumatica and Power BI.
The predicted salary is between 25000 - 32000 £ per year.
CRASTER in Greater London is looking for a Merchandising Assistant to support the Buying and Merchandising teams. This role involves raising purchase orders, tracking data, and building cross-functional relationships to ensure accurate, timely merchandising processes.
The ideal candidate will have strong analytical skills, attention to detail, and be comfortable working with business systems like Acumatica and Power BI. This is a great opportunity for someone eager to grow in a dynamic, design-led company.
Merchandising Assistant - Data-Driven Buying (Hybrid) employer: CRASTER
CRASTER is an exceptional employer located in Greater London, offering a vibrant work culture that fosters creativity and collaboration. Employees benefit from comprehensive growth opportunities, including training in advanced business systems like Acumatica and Power BI, ensuring they are well-equipped for their roles. With a focus on design-led innovation, Craster provides a meaningful and rewarding environment for those looking to make an impact in the merchandising field.
StudySmarter Expert Advice🤫
We think this is how you could land Merchandising Assistant - Data-Driven Buying (Hybrid)
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like CRASTER!
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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 CRASTER.
✨Apply Directly through Our Website
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We think you need these skills to ace Merchandising Assistant - Data-Driven Buying (Hybrid)
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 CRASTER, 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 CRASTER. 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 CRASTER
✨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 CRASTER!
✨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.
