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- Tasks: Analyse fraud trends and enhance prevention tools to protect users.
- Company: Community-driven fashion platform in Greater London with an inclusive culture.
- Benefits: Flexible working options, health support, and a range of other perks.
- Why this job: Play a vital role in enhancing security and making a real difference.
- Qualifications: Experience in fraud analytics and strong SQL skills.
- Other info: Join a dynamic team focused on innovation and user safety.
The predicted salary is between 36000 - 60000 £ per year.
A community-driven fashion platform in Greater London is seeking a Fraud Data Analyst to analyze fraud trends, improve fraud prevention tools, and manage data from various sources. The ideal candidate will have experience in fraud analytics and a high proficiency in SQL. The company promotes an inclusive environment and offers a range of benefits, including flexible working options and health support. This position is crucial to protect users and enhance the platform's security.
Fraud & Payments Risk Analyst (Data-Driven) in London employer: Depop
Contact Detail:
Depop Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Fraud & Payments Risk Analyst (Data-Driven) in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those already working in fraud analytics. A friendly chat can lead to insider info about job openings or even a referral.
✨Tip Number 2
Show off your skills! Prepare a portfolio showcasing your previous work in fraud analytics. Use real examples of how you've tackled fraud trends and improved prevention tools – this will make you stand out!
✨Tip Number 3
Practice makes perfect! Get ready for interviews by brushing up on your SQL skills and understanding common fraud scenarios. Mock interviews with friends can help you feel more confident when it’s your turn.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities waiting for you, and applying directly can sometimes give you an edge. Plus, it shows you’re genuinely interested in joining our community.
We think you need these skills to ace Fraud & Payments Risk Analyst (Data-Driven) in London
Some tips for your application 🫡
Show Off Your SQL Skills: Make sure to highlight your SQL proficiency in your application. We want to see how you've used it in past roles, especially in fraud analytics. Don't just say you're good at it; give us examples!
Demonstrate Your Analytical Mindset: When writing your application, share specific instances where you've analysed fraud trends or improved prevention tools. We love data-driven stories that showcase your problem-solving skills and creativity.
Emphasise Inclusivity: Since we promote an inclusive environment, feel free to mention any experiences you have working in diverse teams or how you’ve contributed to creating a welcoming atmosphere. It’s all about community for us!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates. Plus, it’s super easy!
How to prepare for a job interview at Depop
✨Know Your SQL Inside Out
Since the role requires high proficiency in SQL, make sure you brush up on your SQL skills before the interview. Be prepared to discuss your experience with SQL queries and how you've used them to analyse data in previous roles.
✨Understand Fraud Trends
Research common fraud trends in the fashion industry and be ready to discuss how you would approach analysing these trends. Showing that you’re aware of current issues will demonstrate your passion for the role and the industry.
✨Prepare Real-World Examples
Think of specific examples from your past work where you successfully identified fraud patterns or improved fraud prevention tools. Use the STAR method (Situation, Task, Action, Result) to structure your answers clearly and effectively.
✨Emphasise Team Collaboration
This company values an inclusive environment, so highlight your ability to work well in a team. Share experiences where you collaborated with others to achieve a common goal, especially in data-driven projects related to fraud analytics.
