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- Tasks: Join a dynamic team to analyse data using ML and NLP techniques.
- Company: Leading Quant Trading firm in Greater London with a focus on innovation.
- Benefits: Competitive salary, bonuses, and comprehensive benefits package.
- Why this job: Make an impact in finance by leveraging cutting-edge data science techniques.
- Qualifications: Strong Python skills, mathematical background, and NLP experience required.
- Other info: Collaborative environment with opportunities for professional growth.
The predicted salary is between 150000 - 160000 £ per year.
A leading Quant Trading firm is seeking a Data Scientist in Greater London to join their Cross Asset Data Quant team. In this role, you will collaborate with Quant Researchers and Data Engineering teams, utilizing Machine Learning and Statistical Methods for dataset evaluation and feature extraction.
Candidates must have:
- Strong Python skills
- A solid mathematical background
- Experience in NLP
This opportunity offers a competitive salary of £150,000 - £160,000 plus bonuses and benefits.
Cross-Asset Data Scientist | ML/NLP in Front Office employer: Vertus Partners
Contact Detail:
Vertus Partners Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Cross-Asset Data Scientist | ML/NLP in Front Office
✨Tip Number 1
Network like a pro! Reach out to folks in the Quant Trading space on LinkedIn or at industry events. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your Python projects, especially those involving ML and NLP. This gives us a tangible way to see what you can do beyond the application.
✨Tip Number 3
Prepare for the technical interview! Brush up on your mathematical concepts and be ready to discuss your approach to dataset evaluation and feature extraction. We love seeing how you think!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining our team.
We think you need these skills to ace Cross-Asset Data Scientist | ML/NLP in Front Office
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your strong Python skills and any experience you have with Machine Learning and NLP. We want to see how your background aligns with the role, so don’t hold back!
Tailor Your Application: Take a moment to customise your CV and cover letter for this specific role. Mention your experience in dataset evaluation and feature extraction, as these are key aspects of what we’re looking for.
Be Clear and Concise: When writing your application, keep it clear and to the point. We appreciate well-structured applications that make it easy for us to see your qualifications and fit for the team.
Apply Through Our Website: Don’t forget to apply 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 Cross Asset Data Quant team.
How to prepare for a job interview at Vertus Partners
✨Know Your Python Inside Out
Make sure you brush up on your Python skills before the interview. Be prepared to discuss your experience with libraries like Pandas, NumPy, and Scikit-learn, as well as any projects where you've implemented machine learning algorithms.
✨Brush Up on Your Maths
Since a solid mathematical background is crucial for this role, review key concepts in statistics and probability. Be ready to explain how you've applied these concepts in real-world scenarios, especially in relation to data evaluation and feature extraction.
✨Showcase Your NLP Experience
Prepare to discuss your experience with Natural Language Processing. Think of specific examples where you've used NLP techniques to solve problems or extract insights from text data. This will demonstrate your expertise and relevance to the role.
✨Collaborative Mindset
This position involves working closely with Quant Researchers and Data Engineering teams. Be ready to talk about your teamwork experiences, how you handle feedback, and your approach to collaboration. Highlight any past projects where you successfully worked in a team setting.
