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For Companies At a Glance
- Tasks: Dive into data analysis, building models and optimising strategies with a dynamic team.
- Company: Join a cutting-edge trading firm in London, blending finance and technology.
- Benefits: Enjoy hybrid work options, competitive pay, and performance-based bonuses.
- Why this job: Be part of an innovative culture that values collaboration and creativity in finance.
- Qualifications: Strong Python skills and a background in quantitative fields like Math or Physics required.
- Other info: Experience with C++ and machine learning techniques is a plus.
The predicted salary is between 36000 - 60000 £ 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.
Researcher (Data Analysis) employer: Durlston Partners
Contact Detail:
Durlston Partners Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Researcher (Data Analysis)
✨Tip Number 1
Brush up on your Python skills! Since strong coding ability in Python is a must for this role, consider working on personal projects or contributing to open-source projects that involve data analysis. This will not only enhance your skills but also give you practical examples to discuss during interviews.
✨Tip Number 2
Familiarise yourself with statistical modelling and machine learning techniques. You can do this by taking online courses or reading relevant literature. Being able to speak confidently about these topics will set you apart from other candidates.
✨Tip Number 3
Network with professionals in the quantitative research field. Attend industry meetups, webinars, or conferences where you can connect with researchers and traders. Building relationships can lead to valuable insights and potential referrals.
✨Tip Number 4
Stay updated on market trends and developments in systematic trading. Follow relevant blogs, podcasts, and news sources to ensure you can engage in informed discussions during interviews. Showing your passion for the field can make a significant impression.
We think you need these skills to ace Researcher (Data Analysis)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your Python skills and any experience you have in quantitative fields like Math, Physics, or Computer Science. Include specific projects or roles where you've applied statistical modeling or machine learning techniques.
Craft a Strong Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Mention how your background aligns with their needs, particularly your experience with market data and simulation tools. Be specific about your coding abilities and any relevant projects.
Showcase Relevant Projects: If you have worked on projects related to systematic trading, market making, or high-frequency signals, be sure to include these in your application. Describe your role, the tools you used, and the outcomes of your work.
Highlight Team Collaboration: Since the role involves working closely with researchers, traders, and developers, emphasise any previous experiences where you collaborated in a team setting. This could include group projects, internships, or relevant work experiences.
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 data analysis or model building, and consider bringing examples of your code to demonstrate your skills.
✨Demonstrate Your Statistical Knowledge
Since the role requires a solid foundation in statistics and probability theory, brush up on key concepts and be ready to explain how you've applied them in previous work. You might be asked to solve problems on the spot, so practice common statistical modelling techniques.
✨Understand Market Dynamics
Familiarise yourself with market making and high-frequency trading concepts. Being able to discuss how your research can impact trading strategies will show that you understand the practical applications of your work.
✨Prepare for Technical Questions
Expect technical questions related to machine learning and statistical modelling. Review common algorithms and their applications, and be ready to discuss how you've implemented these techniques in past projects.
