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- Tasks: Develop machine learning solutions to detect fraud in real-time.
- Company: Global bank payment company focused on innovation and security.
- Benefits: Competitive pay, hybrid work model, and comprehensive benefits.
- Why this job: Join a mission-driven team and make a difference in fintech.
- Qualifications: Strong STEM background and experience in predictive modelling.
- Other info: Collaborative environment with opportunities for professional growth.
The predicted salary is between 36000 - 60000 £ per year.
A global bank payment company is seeking a Fraud Prevention Data Scientist to develop ML solutions for fraud detection. You will work closely with engineers and analysts, ensuring models are deployed effectively within the organization's operations.
Ideal candidates will have a strong STEM background, experience with predictive modeling, and a passion for driving innovation in fraud prevention. The position offers a hybrid work model and includes competitive compensation and benefits.
Fraud Prevention Data Scientist — Real-Time ML for Fintech employer: GoCardless
Contact Detail:
GoCardless Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Fraud Prevention Data Scientist — Real-Time ML for Fintech
✨Tip Number 1
Network like a pro! Reach out to professionals in the fintech space on LinkedIn or at industry events. We can’t stress enough how valuable personal connections can be in landing that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your predictive modelling projects and any real-time ML solutions you've developed. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for those interviews! Brush up on your technical knowledge and be ready to discuss your experience with fraud detection models. We recommend practising common interview questions related to data science and machine learning.
✨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 Prevention Data Scientist — Real-Time ML for Fintech
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your STEM background and any relevant experience in predictive modelling. We want to see how your skills align with the role of a Fraud Prevention Data Scientist, so don’t hold back!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Share your passion for driving innovation in fraud prevention and explain why you’re excited about working with us. Keep it concise but impactful.
Showcase Your Projects: If you've worked on any ML projects related to fraud detection or similar fields, make sure to mention them. We love seeing real-world applications of your skills, so include links or descriptions of your work!
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 the role. Plus, it’s super easy!
How to prepare for a job interview at GoCardless
✨Know Your ML Models Inside Out
Make sure you can discuss the machine learning models you've worked with in detail. Be prepared to explain how they work, their strengths and weaknesses, and how you've applied them in real-world scenarios, especially in fraud detection.
✨Showcase Your STEM Background
Highlight your educational background and any relevant projects that demonstrate your analytical skills. Be ready to discuss specific coursework or research that relates to predictive modelling and data analysis.
✨Understand the Fintech Landscape
Familiarise yourself with current trends and challenges in the fintech industry, particularly around fraud prevention. This will show your passion for the field and help you engage in meaningful conversations during the interview.
✨Prepare for Technical Questions
Expect technical questions related to data science and machine learning. Brush up on your coding skills and be ready to solve problems on the spot, as this is crucial for demonstrating your ability to deploy models effectively.
