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- Tasks: Build and optimise trading models while enhancing execution efficiency.
- Company: Leading global systematic hedge fund based in London.
- Benefits: Competitive salary, collaborative environment, and exposure to top Portfolio Managers.
- Why this job: Join a dynamic team and make a real impact in the finance world.
- Qualifications: 2-8 years in quantitative research or trading with strong Python skills.
- Other info: Exciting opportunity for career growth in a fast-paced environment.
The predicted salary is between 48000 - 72000 £ per year.
A leading global systematic hedge fund in London is seeking an experienced Quantitative Researcher to join their central research group. The role involves building and optimising intraday and mid-frequency trading models, improving execution efficiency, and constructing portfolios for centralized strategies.
Candidates should have 2-8 years of experience in quantitative research or trading, strong Python skills, and a background in equities and machine learning. This position offers the opportunity to work closely with Portfolio Managers across various regions.
Intraday Equity Quant Researcher - ML & Execution Optimisation employer: Selby Jennings
Contact Detail:
Selby Jennings Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Intraday Equity Quant Researcher - ML & Execution Optimisation
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those working at hedge funds or in quantitative research. A friendly chat can lead to insider info about job openings and even referrals.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your best projects, especially those involving Python and machine learning. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for technical interviews by brushing up on your quantitative skills and trading models. Practice coding challenges and be ready to discuss your thought process during problem-solving.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities that might just be the perfect fit for you. Plus, it’s a great way to get noticed by our hiring team.
We think you need these skills to ace Intraday Equity Quant Researcher - ML & Execution Optimisation
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your Python skills and any experience you have with equities and machine learning. We want to see how your background aligns with the role, so don’t hold back!
Tailor Your Application: Customise your CV and cover letter to reflect the specific requirements of the Intraday Equity Quant Researcher position. We love seeing candidates who take the time to connect their experiences to what we’re looking for.
Be Clear and Concise: When writing your application, keep it straightforward and to the point. We appreciate clarity, so make sure your key achievements and experiences shine through without unnecessary fluff.
Apply Through Our Website: We encourage you to submit your application directly through our website. It’s the best way for us to receive your details and ensures you’re considered for this exciting opportunity!
How to prepare for a job interview at Selby Jennings
✨Know Your Models Inside Out
Make sure you can discuss the intraday and mid-frequency trading models you've worked on in detail. Be prepared to explain your thought process behind optimising these models and how they improve execution efficiency.
✨Show Off Your Python Skills
Brush up on your Python programming before the interview. Be ready to demonstrate your coding abilities, perhaps even solving a problem on the spot. Highlight any libraries or frameworks you've used that are relevant to quantitative research.
✨Understand the Market Landscape
Familiarise yourself with current trends in equities and machine learning applications in trading. Being able to discuss recent developments or case studies will show your passion and knowledge about the field.
✨Prepare for Collaboration Questions
Since you'll be working closely with Portfolio Managers, think of examples where you've successfully collaborated in the past. Be ready to discuss how you communicate complex quantitative concepts to non-technical stakeholders.
