At a Glance
- Tasks: Transform data into insights that drive commercial decisions in eCommerce.
- Company: Join a dynamic team in the fashion retail industry.
- Benefits: Competitive salary, flexible hours, and opportunities for growth.
- Other info: Collaborative environment with a focus on innovation and efficiency.
- Why this job: Combine your analytical skills with a passion for fashion and make an impact.
- Qualifications: Experience in data analysis and strong knowledge of Google Sheets required.
The predicted salary is between 37000 - 41000 £ per year.
We are looking for a commercially minded Data Analyst & Merchandiser to join our Merchandising team. This role will be responsible for transforming data into actionable insights that drive better commercial decisions across our eCommerce business. The successful candidate will analyse trading performance, develop automated reporting, improve data processes using automated reporting, and support the merchandising team with in-depth analysis of product, customer and inventory performance. They will also be responsible for maintaining accurate online merchandising, ensuring products are optimised across the Shopify website and supporting seasonal planning through detailed reporting and analysis. This is an ideal opportunity for someone who enjoys combining analytical thinking with commercial decision-making and is passionate about improving efficiency through technology and automation.
Key Responsibilities
- Data Analysis & Reporting
- Develop, maintain and continuously improve trading reports and dashboards.
- Analyse sales, inventory and profitability to identify commercial opportunities and risks.
- Build automated reporting solutions using Google Sheets, and reporting software to reduce manual processes.
- Create daily, weekly and monthly performance reports for the merchandising and leadership teams.
- Identify trends, anomalies and actionable insights to support trading decisions.
- Produce post-season reviews and performance summaries with clear recommendations.
- Process Improvement
- Identify opportunities to automate reporting and repetitive merchandising tasks using Google Sheets or Power BI.
- Build and maintain automated reporting workflows.
- Improve data accuracy and reporting efficiency through automation.
- Introduce new reporting methods that enable faster commercial decision-making.
- Size & Fit Analysis
- Analyse sales by size to identify demand patterns and opportunities.
- Monitor size sell-through and stock availability.
- Produce size curve recommendations for future buys.
- Highlight sizing issues affecting conversion or customer returns.
- Support buying and merchandising teams with size planning recommendations.
- Seasonal & Product Performance Analysis
- Produce seasonal performance reviews by category, product and collection.
- Analyse sell-through, markdown performance and stock efficiency.
- Review newness performance and identify opportunities for future range planning.
- Analyse lifecycle performance from launch through end-of-season.
- Provide recommendations for future assortment planning.
- Commercial Support
- Support weekly trade meetings with meaningful commercial insights.
- Assist in forecasting sales and inventory performance.
- Work closely with Merchandising, Buying, Digital Marketing and Finance.
- Help identify opportunities to improve sales, margin and inventory productivity.
Skills & Experience
- Essential
- Experience in a data analyst, merchandising analyst or online merchandising role.
- Strong analytical skills with the ability to interpret large datasets.
- Advanced knowledge of Google Sheets, including: QUERY, ARRAYFORMULA, FILTER, INDEX/MATCH, XLOOKUP, Pivot Tables, Dashboards.
- Experience using Shopify.
- Excellent Excel skills.
- Strong commercial awareness within retail or fashion.
- Ability to present data clearly and communicate insights to non-technical stakeholders.
- Highly organised with excellent attention to detail.
- Desirable
- Knowledge of Looker Studio, Power BI or Tableau.
- SQL knowledge.
- Experience with Google Analytics (GA4).
- Understanding of inventory planning and merchandising principles.
- Experience working with APIs or automated data integrations.
Key Performance Indicators (KPIs)
- Accuracy and timeliness of reporting.
- Reduction in manual reporting through automation.
- Improvement in reporting efficiency.
- Increased visibility of key trading metrics.
- Accuracy of size recommendations.
- Quality of seasonal analysis and commercial recommendations.
- Improvement in stock productivity and sell-through.
- Stakeholder satisfaction with reporting and insights.
Personal Attributes
- Naturally curious with a passion for solving problems through data.
- Commercially minded with an interest in fashion and retail.
- Proactive and continually looking for ways to improve processes.
- Comfortable working with large volumes of data.
- Strong communication and presentation skills.
- Able to manage multiple priorities in a fast-paced environment.
- Collaborative team player who enjoys working cross-functionally.
Merchandiser Data Analyst employer: Self-Portrait
Self-Portrait offers an exceptional work environment in the heart of Mayfair, where creativity and passion for luxury fashion thrive. Employees benefit from a supportive culture that encourages personal growth and development, alongside competitive remuneration and unique perks associated with working in a prestigious location. Join our dynamic team and be part of a brand that values your contributions and fosters lasting relationships with customers.
StudySmarter Expert Advice🤫
We think this is how you could land Merchandiser Data Analyst
✨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 Self-Portrait.
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We think you need these skills to ace Merchandiser Data Analyst
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 Self-Portrait, 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 Self-Portrait. 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 Self-Portrait
✨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 Self-Portrait!
✨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.
