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For Companies At a Glance
- Tasks: Analyse billions of transactions and develop machine learning models to combat fraud.
- Company: Join LexisNexis Risk Solutions, a leader in risk management.
- Benefits: Enjoy generous holidays, health screening, and a competitive pension scheme.
- Other info: Dynamic team environment with opportunities for professional growth.
- Why this job: Make a real impact in fraud prevention while enhancing customer profitability.
- Qualifications: Proficient in SQL and Python with experience in data analysis.
The predicted salary is between 50000 - 70000 £ per year.
LexisNexis Risk Solutions in the United Kingdom seeks a data scientist skilled in SQL and Python to join a team analyzing billions of transactions monthly. This role will focus on developing machine learning models to combat fraud and improve customer profitability. The ideal candidate has experience in the sector and is adept at transforming complex data into actionable insights.
Benefits include:
- Generous holidays
- Health screening
- Competitive pension scheme
Fraud Data Scientist: Real-Time Risk & ML employer: LexisNexis Risk Solutions
Contact Detail:
LexisNexis Risk Solutions Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Fraud Data Scientist: Real-Time Risk & ML
✨Tip Number 1
Network like a pro! Reach out to professionals in the fraud detection and data science fields on LinkedIn. Join relevant groups and participate in discussions to get your name out there.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your SQL and Python projects, especially those related to machine learning and fraud detection. This will give potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills and understanding the latest trends in fraud prevention. Be ready to discuss how you can turn complex data into actionable insights.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Fraud Data Scientist: Real-Time Risk & ML
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your SQL and Python skills in your application. We want to see how you've used these tools in real-world scenarios, especially in fraud detection or data analysis.
Be Specific About Your Experience: When detailing your past roles, focus on your experience with machine learning models and how they’ve helped improve customer profitability. We love seeing concrete examples of your work!
Keep It Clear and Concise: We appreciate a well-structured application. Use bullet points where possible and keep your language straightforward. This helps us quickly grasp your qualifications and fit for the role.
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for the position.
How to prepare for a job interview at LexisNexis Risk Solutions
✨Know Your SQL and Python Inside Out
Make sure you brush up on your SQL and Python skills before the interview. Be ready to discuss specific projects where you've used these languages, especially in relation to data analysis and machine learning. Practising coding challenges can also help you feel more confident.
✨Understand the Fraud Landscape
Familiarise yourself with current trends and challenges in fraud detection. Research LexisNexis Risk Solutions and their approach to combating fraud. This will show your genuine interest in the role and help you articulate how your skills can contribute to their mission.
✨Prepare for Technical Questions
Expect technical questions that assess your problem-solving abilities and understanding of machine learning models. Prepare to explain your thought process clearly and concisely. It might be helpful to review common algorithms and their applications in fraud detection.
✨Showcase Your Data Storytelling Skills
Be ready to demonstrate how you've transformed complex data into actionable insights in your previous roles. Use examples that highlight your ability to communicate findings effectively, as this is crucial for improving customer profitability in the role.