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- Tasks: Develop software for trading simulations and evaluate new technologies.
- Company: Dynamic trading firm with a focus on systematic research.
- Benefits: Mentoring, fast-paced environment, and opportunities for professional growth.
- Why this job: Join a cutting-edge team and make an impact in quantitative finance.
- Qualifications: Bachelor's in Computer Science, strong C++ and Python skills, and finance maths knowledge.
- Other info: Exciting career development in a collaborative atmosphere.
The predicted salary is between 36000 - 60000 £ per year.
A diversified trading firm is seeking a Research Engineer to join their systematic research team. The position involves developing software for trading strategy simulations, large-scale data acquisition, and evaluating new technologies.
Candidates should have a Bachelor's degree in Computer Science or related field, with strong skills in C++ and Python, along with a good understanding of quantitative finance mathematics. This role offers opportunities for development and mentoring in a fast-paced environment.
Quantitative Research Engineer in London employer: DRW
Contact Detail:
DRW Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Quantitative Research Engineer in London
✨Tip Number 1
Network like a pro! Reach out to professionals in the trading and finance sectors on LinkedIn. Join relevant groups and engage in discussions to get your name out there.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects in C++ and Python, especially those related to quantitative finance. This will give you an edge during interviews.
✨Tip Number 3
Prepare for technical interviews by brushing up on algorithms and data structures. Practice coding challenges on platforms like LeetCode or HackerRank to sharpen your problem-solving skills.
✨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 Quantitative Research Engineer in London
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your C++ and Python skills in your application. We want to see how you’ve used these languages in past projects, especially if they relate to trading or data analysis.
Quantitative Finance Knowledge is Key: Don’t forget to mention your understanding of quantitative finance mathematics. We’re looking for candidates who can demonstrate their grasp of the concepts that drive our trading strategies.
Tailor Your Application: Take a moment to customise your CV and cover letter for this role. We love seeing how your experiences align with what we do at StudySmarter, so make it personal!
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 DRW
✨Know Your Tech Inside Out
Make sure you brush up on your C++ and Python skills before the interview. Be ready to discuss specific projects where you've used these languages, and think about how you can apply them to trading strategy simulations.
✨Brush Up on Quantitative Finance
Since the role requires a good understanding of quantitative finance mathematics, review key concepts and be prepared to explain how they relate to trading strategies. This will show that you’re not just a coding whiz but also understand the financial implications of your work.
✨Prepare for Problem-Solving Questions
Expect to face technical questions or coding challenges during the interview. Practice solving problems on platforms like LeetCode or HackerRank, focusing on algorithms and data structures relevant to trading applications.
✨Show Enthusiasm for Learning
This position offers development and mentoring opportunities, so express your eagerness to learn new technologies and grow within the team. Share examples of how you've embraced learning in the past, whether through courses, projects, or self-study.
