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- Tasks: Research advanced machine learning techniques to model market behaviour and develop predictive models.
- Company: Leading global hedge fund with a focus on systematic research.
- Benefits: Competitive salary, collaborative environment, and direct impact on trading strategies.
- Why this job: Combine cutting-edge research with real-world trading impact in a high-performance team.
- Qualifications: PhD or Master's degree, strong programming skills in Python, and machine learning expertise.
- Other info: Exciting opportunity for career growth in a dynamic financial environment.
The predicted salary is between 43200 - 72000 £ per year.
A leading global hedge fund is seeking a Quantitative Machine Learning Researcher to join its systematic research group. The role involves researching advanced machine learning techniques to model market behavior and develop predictive models.
Ideal candidates will have:
- A PhD or a Master's degree with experience
- A robust programming background in Python
- A strong foundation in machine learning
This unique opportunity combines research with direct impact on trading strategies in a collaborative, high-performance environment.
Quantitative ML Researcher for Live Trading Alpha employer: HWTS Global
Contact Detail:
HWTS Global Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Quantitative ML Researcher for Live Trading Alpha
✨Tip Number 1
Network like a pro! Reach out to professionals in the hedge fund and machine learning space. Use platforms like LinkedIn to connect with current employees at the firm you're eyeing. A friendly chat can sometimes lead to insider info or even a referral!
✨Tip Number 2
Show off your skills! Prepare a portfolio showcasing your best projects in Python and machine learning. Whether it's predictive models or market behaviour analyses, having tangible examples will make you stand out during interviews.
✨Tip Number 3
Practice makes perfect! Brush up on your technical interview skills by solving quantitative problems and coding challenges. Websites like LeetCode or HackerRank can be super helpful for this. We want you to feel confident when it’s time to shine!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who take the initiative. Don’t forget to tailor your application to highlight your research experience and programming prowess!
We think you need these skills to ace Quantitative ML Researcher for Live Trading Alpha
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your programming prowess in Python and any machine learning experience you have. We want to see how your skills can contribute to our systematic research group!
Tailor Your Application: Don’t just send a generic CV! Customise your application to reflect the specific requirements of the Quantitative ML Researcher role. We love seeing candidates who take the time to align their experiences with what we’re looking for.
Be Clear and Concise: When writing your application, keep it straightforward. We appreciate clarity and brevity, so make sure your key points stand out without unnecessary fluff.
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 don’t miss out on any important updates from our team!
How to prepare for a job interview at HWTS Global
✨Know Your ML Fundamentals
Brush up on your machine learning concepts and algorithms. Be ready to discuss how you’ve applied these techniques in real-world scenarios, especially in trading or market behaviour modelling.
✨Showcase Your Coding Skills
Since a strong programming background in Python is essential, prepare to demonstrate your coding skills. You might be asked to solve a problem on the spot, so practice coding challenges related to data analysis and algorithm implementation.
✨Prepare for Case Studies
Expect to tackle case studies that reflect the role's responsibilities. Familiarise yourself with common market scenarios and think about how you would apply machine learning to develop predictive models in those situations.
✨Emphasise Collaboration
This role is in a collaborative environment, so highlight your teamwork experiences. Share examples of how you’ve worked with others to achieve research goals or improve trading strategies, showcasing your ability to contribute to a high-performance team.
