Find a job 
Featured Companies 
Featured Universities 
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: Be part of a dynamic team focused on innovation and growth.
- Why this job: Make a real impact in fraud prevention while enhancing customer profitability.
- Qualifications: Experience in SQL and Python, with a knack for turning data into insights.
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, and a competitive pension scheme.
Fraud Data Scientist: Real-Time Risk & ML in London 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 in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at local meetups. We all know that sometimes it’s not just what you know, but who you know that can help you land that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your SQL and Python projects, especially those related to fraud detection or machine learning. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills and understanding the latest trends in fraud analytics. We recommend practising common data science interview questions and even doing mock interviews with friends to build confidence.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. 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 in London
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.
Tell Your Story: Don’t just list your experiences; tell us how you transformed complex data into actionable insights. We love hearing about your journey and the impact you've made in previous roles.
Tailor Your Application: Customise your CV and cover letter for this role. Mention specific projects or experiences that relate to developing machine learning models and combating fraud. It shows us you're genuinely interested!
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 with us!
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 prepared 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. Be ready to explain your thought process and the rationale behind your decisions. Practising with mock interviews can help you articulate your knowledge clearly.
✨Showcase Your Data Storytelling Skills
Since the role involves transforming complex data into actionable insights, prepare examples of how you've done this in the past. Use the STAR method (Situation, Task, Action, Result) to structure your responses, highlighting your ability to communicate findings effectively.