At a Glance
- Tasks: Analyse data to uncover insights and drive strategic decisions for iconic brands.
- Company: Join Condé Nast, a global media leader with a collaborative culture.
- Benefits: Enjoy 25 days holiday, private healthcare, remote work options, and personal growth opportunities.
- Other info: Dynamic work environment with excellent career development and a dog-friendly office.
- Why this job: Make an impact by transforming data into compelling stories that shape business strategies.
- Qualifications: Proficient in SQL, strong analytical skills, and a background in eCommerce preferred.
The predicted salary is between 45000 - 55000 £ per year.
Condé Nast is a global media company with iconic brands across multiple markets.
Location: London, GB
Condé Nast thrives on collaboration and is headquartered in New York and London.
The Role
To be successful in this role, you must be highly strategic with exceptional ability to translate data into compelling narratives and actionable recommendations. You’re equally comfortable building frameworks for recurring analysis as you are diving into ad hoc investigations that uncover hidden opportunities. You have strong technical capabilities—able to troubleshoot data pipeline issues, write SQL queries, and coordinate with engineering teams to resolve systemic challenges. You thrive in fast-paced environments, managing multiple stakeholders across markets and time zones while maintaining rigorous standards for data accuracy and insight quality.
What will you be doing?
- Strategic Analysis & Business Support
- Support regional leads on monthly brand performance reviews across Western Europe markets, empowering teams to understand their data and developing clear recommendations to move performance forward.
- Conduct ad hoc deep-dive analyses using data outside standard dashboards to identify growth opportunities, optimization levers, and emerging trends.
- Analyze monetization opportunities to grow EPC through category classification, commission structures, network performance, GMV, AOV, and other key levers.
- Partner with finance on revenue reporting, interpreting commerce trends and providing context to support financial planning.
- Data Infrastructure & Technical Operations
- Build and maintain intuitive self-service dashboards and data visualizations; set up new reporting systems to help teams access and understand their data.
- Identify and diagnose data discrepancies or pipeline issues; work directly with engineering teams to implement fixes.
- Use SQL to conduct custom analysis and troubleshoot technical challenges; ensure data quality across all reporting systems.
- Process Development & Cross-Functional Coordination
- Create repeatable frameworks and methodologies for strategic analysis; standardize performance review processes across markets.
- Liaise across stakeholders in multiple markets, synthesizing inputs and translating complex technical concepts for non-technical audiences.
- Establish clear protocols for cross-functional collaboration and coordinate effectively across different time zones.
Who you are:
- Highly strategic thinker with exceptional data storytelling abilities—you don’t just present numbers, you build narratives that drive decision‑making.
- Strong technical foundation with proficiency in SQL (required); experience with Python, Tableau, Business Objects, or similar analytical tools is a plus.
- Advanced in Excel/Google Sheets with ability to build sophisticated models and analyses.
- Demonstrated ability to troubleshoot technical issues and coordinate with engineering teams to implement solutions.
- Understanding of GDPR and other local laws regarding data usage.
- Experience developing repeatable processes and frameworks for strategic analysis.
- Proven track record of managing multiple stakeholders across different markets and organizational levels.
- Strong problem‑solving skills with ability to work independently on complex, ambiguous challenges.
- Excellent verbal and written communication skills, able to distill complex analysis into clear, actionable recommendations.
- Background in eCommerce operations; affiliate marketing experience (preferably publisher‑side) is highly valued.
- Deep understanding of conversion funnel dynamics, margin management, and affiliate KPIs.
- Degree in Maths, Computer Science, Economics, or similar quantitative field preferred.
What benefits do we offer?
- 25 days holiday (plus bank holidays) and extra days of annual leave if you move house or want to volunteer.
- Competitive pension scheme, Bupa Private Healthcare, Season ticket loans and eye tests.
- Tools to support wellbeing, including core hours, 10 remote days (from home or a country with a Condé Nast office location), Employee Assistance Programme, corporate gym membership and cycle to work scheme.
- Dog friendly office and discounts and magazine subscriptions to keep you up to date with all things Condé Nast.
- Personal and professional growth through the Condé Nast Learning Hub with a portfolio of learning courses and training available in local languages.
- Employee Resource Groups provide a platform for employees to identify shared objectives, exchange ideas, and work on community priorities for our global workforce.
Condé Nast is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, age, familial status and other legally protected characteristics.
Commerce Data Analyst in London employer: Dormont Manufacturing Co
Condé Nast is an exceptional employer that fosters a collaborative and innovative work culture in the heart of London. With a strong focus on employee well-being, the company offers generous benefits such as 25 days of holiday, private healthcare, and opportunities for personal and professional growth through its Learning Hub. Employees thrive in a dynamic environment where they can leverage their analytical skills to drive impactful business decisions while enjoying unique perks like a dog-friendly office and discounts on iconic publications.
StudySmarter Expert Advice🤫
We think this is how you could land Commerce Data Analyst in London
✨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 Dormont Manufacturing Co!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Commerce Data Analyst at Dormont Manufacturing Co.
✨Leverage Professional Networks
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 Dormont Manufacturing Co.
✨Apply Directly through Our Website
When you find a suitable opening like Commerce Data Analyst at Dormont Manufacturing Co, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Commerce Data Analyst in London
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 Dormont Manufacturing Co, 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 Dormont Manufacturing Co. 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 Dormont Manufacturing Co
✨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 Dormont Manufacturing Co!
✨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.