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- Tasks: Train and deploy machine learning models to detect fraud in real-time.
- Company: Leading fraud detection company with a focus on innovation.
- Benefits: Flexible hours, remote work, wellness budget, and generous holiday and health benefits.
- Why this job: Join a mission-driven team and make a difference in fraud prevention.
- Qualifications: Experience in Python and machine learning, with strong analytical and communication skills.
- Other info: Enjoy a supportive remote-first environment with opportunities for growth.
The predicted salary is between 36000 - 60000 £ per year.
A fraud detection company is seeking a pragmatic Data Scientist to train, deploy, debug, and evaluate fraud detection models. The ideal candidate will have significant experience in building and deploying machine learning models using Python, strong analytical skills, and the ability to communicate complex ideas effectively.
This role offers flexible working hours, a remote-first environment, and an annual budget for wellness and learning, along with generous holiday and health benefits.
Fraud-Detection Data Scientist | Real-Time ML in London employer: Ravelin Technology Ltd
Contact Detail:
Ravelin Technology Ltd Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Fraud-Detection Data Scientist | Real-Time ML in London
✨Tip Number 1
Network like a pro! Reach out to folks in the fraud detection and data science space on LinkedIn. Join relevant groups, attend webinars, and don’t be shy about asking for informational interviews. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those related to fraud detection. Use platforms like GitHub to share your code and explain your thought process. This will help potential employers see your practical experience in action.
✨Tip Number 3
Prepare for the interview by brushing up on your technical skills. Be ready to discuss your experience with Python and machine learning models. Practice explaining complex concepts in simple terms, as communication is key in this role. We want to see how you can make your ideas accessible!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who take the initiative to engage directly with us. So, get your application in and let’s get the conversation started!
We think you need these skills to ace Fraud-Detection Data Scientist | Real-Time ML in London
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your experience with Python and machine learning in your application. We want to see how you've built and deployed models in the past, so don’t hold back on those details!
Keep It Clear and Concise: When explaining your projects or experiences, use straightforward language. We appreciate candidates who can communicate complex ideas simply, so aim for clarity in your writing.
Tailor Your Application: Take a moment to customise your application for this role. Mention specific experiences that relate to fraud detection and real-time ML, as it shows us you’re genuinely interested in what we do.
Apply Through Our Website: We encourage you 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 role without any hiccups!
How to prepare for a job interview at Ravelin Technology Ltd
✨Know Your Models Inside Out
Make sure you can discuss the machine learning models you've built and deployed in detail. Be ready to explain your approach to training, debugging, and evaluating these models, especially in the context of fraud detection.
✨Show Off Your Analytical Skills
Prepare to showcase your analytical skills by discussing specific examples where you've solved complex problems using data. Use real-world scenarios to illustrate how your insights led to successful outcomes in previous roles.
✨Communicate Clearly and Confidently
Since the role requires effective communication of complex ideas, practice explaining your work in simple terms. Think about how you would describe your projects to someone without a technical background, as this will demonstrate your ability to bridge the gap between data science and business needs.
✨Embrace the Remote Environment
Familiarise yourself with remote working tools and practices. Be prepared to discuss how you manage your time and collaborate with teams in a remote-first environment, highlighting any experience you have in similar setups.
