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- Tasks: Join us as a Quantitative Researcher to build and optimise trading models.
- Company: We are a dynamic trading firm based in London, embracing innovation and technology.
- Benefits: Enjoy competitive compensation with performance-based bonuses and hybrid work options.
- Why this job: Dive into a fast-paced environment where your coding skills can shine and make an impact.
- Qualifications: Strong Python skills and a background in Math, Physics, or Computer Science required.
- Other info: Experience with statistical modelling or machine learning techniques is a plus.
The predicted salary is between 43200 - 72000 £ per year.
We’re hiring a Quantitative Researcher with strong Python skills and a solid foundation in statistics and probability theory. You’ll work closely with researchers, traders, and developers to build and optimize models, using large-scale market data and simulation tools. Depending on your experience, you will either focus on market making and high-frequency signals or machine learning-driven signal research and automation.
- Strong coding ability in Python (C++ is a plus)
- Background in a quantitative field (Math, Physics, CS, etc.)
- Experience with statistical modeling and/or ML techniques
Competitive compensation, including performance-based upside.
Researchers employer: Durlston Partners
Contact Detail:
Durlston Partners Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Researchers
✨Tip Number 1
Brush up on your Python skills, as this role requires strong coding ability. Consider working on personal projects or contributing to open-source projects that involve quantitative analysis to showcase your expertise.
✨Tip Number 2
Familiarise yourself with statistical modelling and machine learning techniques relevant to trading. You could take online courses or read up on recent research papers to deepen your understanding and be able to discuss these topics confidently during interviews.
✨Tip Number 3
Network with professionals in the quantitative finance field. Attend industry meetups or webinars where you can connect with researchers and traders, which may lead to valuable insights and potential referrals for the position.
✨Tip Number 4
Prepare to discuss your experience with large-scale market data and simulation tools. Think of specific examples from your past work or studies where you successfully applied these skills, as this will demonstrate your practical knowledge during the interview.
We think you need these skills to ace Researchers
Some tips for your application 🫡
Highlight Relevant Skills: Make sure to emphasise your strong Python skills and any experience you have with C++. Clearly outline your background in quantitative fields such as Mathematics, Physics, or Computer Science.
Showcase Your Experience: Detail your experience with statistical modelling and machine learning techniques. Provide specific examples of projects or research where you've applied these skills, especially in relation to market data or trading.
Tailor Your CV: Customise your CV to reflect the job description. Use keywords from the posting, such as 'systematic trading', 'market making', and 'high-frequency signals' to ensure your application stands out.
Craft a Compelling Cover Letter: Write a cover letter that not only explains why you're interested in the role but also how your skills and experiences align with the company's needs. Mention your passion for quantitative research and how you can contribute to their team.
How to prepare for a job interview at Durlston Partners
✨Showcase Your Python Skills
Make sure to highlight your proficiency in Python during the interview. Be prepared to discuss specific projects where you've used Python for quantitative analysis or model building, as this is a key requirement for the role.
✨Demonstrate Your Statistical Knowledge
Brush up on your statistics and probability theory. Expect questions that test your understanding of these concepts, so be ready to explain how you've applied them in past research or projects.
✨Prepare for Technical Questions
Since the role involves working with statistical models and machine learning techniques, anticipate technical questions related to these areas. Practise explaining complex concepts in simple terms, as you may need to communicate your ideas to non-technical team members.
✨Discuss Collaboration Experience
This position requires working closely with researchers, traders, and developers. Be prepared to share examples of how you've successfully collaborated in a team setting, highlighting your communication skills and ability to work towards common goals.
