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- Tasks: Develop cutting-edge trading strategies using advanced quantitative methods and machine learning.
- Company: Leading quantitative trading firm in London with a collaborative culture.
- Benefits: Competitive compensation, professional development, and a dynamic work environment.
- Why this job: Make an impact in global markets while working with innovative technology.
- Qualifications: Strong academic background in probability, statistics, and machine learning experience.
- Other info: Exciting opportunities for career growth in a fast-paced industry.
The predicted salary is between 43200 - 72000 £ per year.
A leading quantitative trading firm in London is seeking a Quantitative Researcher/Analyst to develop effective trading strategies through advanced quantitative methods and technology. The ideal candidate will have a strong academic background, solid expertise in probability and statistics, and practical experience in machine learning applied to financial data.
Join a collaborative environment with competitive compensation and opportunities for professional development while contributing to innovative strategies in global markets.
Quant Researcher: ML‐Driven Trading Models in London employer: HUG
Contact Detail:
HUG Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Quant Researcher: ML‐Driven Trading Models in London
✨Tip Number 1
Network like a pro! Reach out to professionals in the quantitative 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 machine learning projects and trading strategies. This gives us a tangible way to see what you can bring to the table.
✨Tip Number 3
Prepare for those interviews! Brush up on your probability, statistics, and machine learning concepts. We love candidates who can discuss their thought process and problem-solving approach.
✨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 Quant Researcher: ML‐Driven Trading Models in London
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your academic background and any relevant experience in probability, statistics, and machine learning. We want to see how your skills can contribute to our innovative trading strategies!
Tailor Your Application: Don’t just send a generic CV and cover letter. Take the time to tailor your application to the role of Quant Researcher. Mention specific projects or experiences that align with the job description – it’ll make you stand out!
Be Clear and Concise: When writing your application, keep it clear and to the point. We appreciate well-structured documents that are easy to read. Avoid jargon unless it’s relevant to the role – we want to understand your thought process!
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 shows you’re keen on joining our team!
How to prepare for a job interview at HUG
✨Brush Up on Your Maths
Make sure you're comfortable with probability and statistics concepts. Review key formulas and their applications in trading strategies, as you might be asked to solve problems or explain your thought process during the interview.
✨Showcase Your Machine Learning Skills
Prepare to discuss your practical experience with machine learning, especially how you've applied it to financial data. Be ready to share specific projects or models you've worked on, highlighting the impact they had on trading outcomes.
✨Understand the Firm's Trading Strategies
Research the firm's existing trading strategies and be prepared to discuss how your skills can contribute to their success. This shows your genuine interest in the company and helps you align your answers with their goals.
✨Practice Collaborative Problem-Solving
Since the role involves working in a collaborative environment, practice discussing complex problems with others. You might be put in a scenario where you need to work through a problem with the interviewers, so demonstrating teamwork is key.
