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- Tasks: Investigate streaming fraud patterns and analyse data to enhance anti-fraud strategies.
- Company: Major music company in the UK with a focus on innovation.
- Benefits: Comprehensive benefits including medical insurance and pension schemes.
- Why this job: Join a dynamic team and make a real impact in the music industry.
- Qualifications: Strong analytical skills, experience with SQL and Python, and attention to detail.
- Other info: Entry-level full-time position with opportunities for growth.
The predicted salary is between 28800 - 48000 £ per year.
A major music company in the United Kingdom is looking for a Streaming Fraud Data Research & Analyst. The role involves investigating and documenting streaming fraud patterns, analyzing data, and collaborating with various teams to enhance anti-fraud strategies.
Candidates should have strong analytical skills, experience with SQL and Python, and attention to detail.
This entry-level full-time position comes with comprehensive benefits, including medical insurance and pension schemes.
Streaming Fraud Data Analyst - Remote employer: Universal Music Group
Contact Detail:
Universal Music Group Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Streaming Fraud Data Analyst - Remote
✨Tip Number 1
Network like a pro! Reach out to folks in the music industry or data analysis circles. LinkedIn is your best mate here – connect, engage, and don’t be shy to ask for informational chats.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your SQL and Python projects. Whether it’s a personal project or something from your studies, having tangible examples can really set you apart.
✨Tip Number 3
Prepare for those interviews! Research common questions for data analyst roles and practice your responses. Don’t forget to brush up on streaming fraud trends – showing you’re informed can give you an edge.
✨Tip Number 4
Apply through our website! We’ve got loads of opportunities, and applying directly can sometimes give you a better shot. Plus, it shows you’re genuinely interested in being part of our team!
We think you need these skills to ace Streaming Fraud Data Analyst - Remote
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your analytical skills and any experience with SQL and Python. We want to see how your background fits the Streaming Fraud Data Analyst role, so don’t be shy about showcasing relevant projects or coursework!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about tackling streaming fraud and how your skills can help us enhance our anti-fraud strategies. Keep it concise but impactful!
Show Off Your Attention to Detail: In this role, attention to detail is key. When submitting your application, double-check for any typos or errors. A polished application shows us you care about quality and accuracy, which is crucial in data analysis.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, you’ll find all the info you need about the role and our company culture there!
How to prepare for a job interview at Universal Music Group
✨Know Your Data Tools
Make sure you brush up on your SQL and Python skills before the interview. Be ready to discuss how you've used these tools in past projects or coursework, as this will show your technical proficiency and readiness for the role.
✨Understand Streaming Fraud
Do some research on common streaming fraud patterns and current trends in the music industry. Being able to discuss specific examples will demonstrate your interest in the field and your analytical mindset.
✨Collaboration is Key
Since the role involves working with various teams, think of examples where you've successfully collaborated with others. Prepare to share how you communicate and work together to solve problems, as teamwork is crucial in this position.
✨Attention to Detail Matters
Highlight your attention to detail by preparing for potential case studies or data analysis questions during the interview. Show that you can spot anomalies in data and explain your thought process clearly, as this will be vital in identifying fraud patterns.
