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- Tasks: Analyse complex datasets and build dashboards to enhance fraud risk strategies.
- Company: Leading fraud prevention firm with a focus on data-driven insights.
- Benefits: Generous compensation and fully remote work environment.
- Why this job: Join a dynamic team and make a real impact in fraud prevention.
- Qualifications: 7+ years in data roles, strong SQL and Python skills required.
- Other info: Opportunity to work with stakeholders and drive data-driven decisions.
The predicted salary is between 43200 - 72000 £ per year.
A prominent fraud prevention firm is seeking a data-driven professional to enhance the performance of risk strategies. The ideal candidate will analyze complex datasets, build dashboards, and work with stakeholders to make data-driven decisions.
Requirements include:
- Over 7 years in data-centric roles
- Strong SQL and Python skills
- Experience in the fraud/risk domain
This position offers generous compensation and a fully remote work environment.
Data Scientist, Fraud Risk & Insights (Remote) in London employer: Sardine
Contact Detail:
Sardine Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist, Fraud Risk & Insights (Remote) in London
✨Tip Number 1
Network like a pro! Reach out to professionals in the fraud and risk domain on LinkedIn. Join relevant groups and engage in discussions to get your name out there and learn about potential job openings.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data analysis projects, dashboards, and any relevant work you've done with SQL and Python. This will give you an edge when talking to potential employers.
✨Tip Number 3
Prepare for interviews by brushing up on common data science questions and case studies related to fraud risk. Practising your responses will help you feel more confident and ready to impress.
✨Tip Number 4
Don’t forget to apply through our website! We have loads of opportunities that might be perfect for you. Plus, it’s a great way to ensure your application gets seen by the right people.
We think you need these skills to ace Data Scientist, Fraud Risk & Insights (Remote) in London
Some tips for your application 🫡
Show Off Your Data Skills: Make sure to highlight your experience with SQL and Python in your application. We want to see how you've used these skills in real-world scenarios, especially in fraud or risk contexts.
Tailor Your Application: Don’t just send a generic CV! We love it when candidates tailor their applications to the role. Mention specific projects or experiences that relate to enhancing risk strategies and working with complex datasets.
Be Clear and Concise: When writing your application, keep it clear and to the point. We appreciate well-structured responses that get straight to the heart of your qualifications and experiences without unnecessary fluff.
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’re considered for this exciting opportunity in our remote team!
How to prepare for a job interview at Sardine
✨Know Your Data Inside Out
Make sure you’re well-versed in the datasets relevant to fraud risk. Brush up on your SQL and Python skills, and be ready to discuss how you've used them in past roles. Prepare examples of how your data analysis has led to actionable insights.
✨Showcase Your Dashboard Skills
Since building dashboards is part of the role, come prepared with examples of dashboards you've created. Be ready to explain the thought process behind your design choices and how they helped stakeholders make informed decisions.
✨Understand the Fraud Landscape
Familiarise yourself with current trends and challenges in the fraud prevention industry. This will not only show your passion for the field but also help you engage in meaningful conversations with interviewers about potential strategies.
✨Prepare for Stakeholder Scenarios
Think about times when you’ve worked with stakeholders to implement data-driven decisions. Be ready to discuss how you communicated complex data insights in a way that was easily understood and actionable for non-technical team members.
